L. 1-8

M.
Sc (IT) PART-II PAPER: MS(A)-213

SOFTWARE ENGINEERING
LESSON No. 1 AUTHOR: VISHAL SINGH
Software Engineering: Introduction
1.0 Introduction
2.0 Software engineering goals
3.0 Software engineering principles
4.0 The Role of Software Engineering
5.0 History
6.0 The Role of Software Engineer
7.0 The relationship of Software Engineering to other areas of Computer
science
7.1 Programming languages
7.2 Operating systems
7.3 Databases
7.4 Artificial Intelligence
7.5 Theoretical models
8.0 The relationship of Software Engineering to Other Disciplines
8.1 Management science
8.2 System engineering
9.0 Summary
10.0 Self check exercise
11.0 Suggested readings
Objectives:
In this lesson we will study the meaning of Software engineering, its history, role
and importance and the Software life cycle. We will also study the relationship of
Software Engineering to other areas of Computer science
1.0 Introduction
Software engineering is the field of computer science that deals with the
building of software systems which are so large or so complex that they are built
by a team or teams of engineers. Usually, these software systems exist in multiple
versions and are used for many years. During their lifetime, they undergo many
changes-to fix defects, to enhance existing features, to add new features, to remove old
features or to be adapted to run in a new environment.
Parnas has defined software engineering as "multi-person construction of
multi-version software." This definition captures the essence of software engineering
and highlights the differences between programming and software engineering. A
M.Sc.(IT) Part-II 2 Paper : MS(A)-213
programmer writes a complete program, while a software engineer writes a software

component that will be combined with components written by other software engineers
to build a system. The component one writes may be modified by others; it may be
used by others to build different versions of the system long after one has left the
project. Programming is primarily a personal activity, while software engineering is
essentially a team activity.
Software Engineering is the use of techniques, methods and methodologies to
develop high quality software which is
 Reliable
 Easy to understand
 Useful
 Modular
 Efficient
 Modifiable
 Reusable
 Good user interface
 Well documented
 Delivered in cost effective and timely manner
Software engineering is a profession dedicated to designing, implementing and
modifying software so that it is of high quality, affordable, maintainable and fast to
build. It is a systematic approach to the analysis, designing, implementation and
reengineering of software, that is, the application of engineering to software. Software
engineering has made progress since the 1960s. There are standard techniques that
are used in the field. But the field is still far from achieving the status of a classic
engineering discipline. Many areas remain in the field that are still being taught and
practiced on the basis of informal techniques. There are no generally accepted methods
even for specifying what a software system should do. In designing an electrical
engineering system, such as an amplifier, the system is specified precisely. All
parameters and tolerance levels are stated clearly and are understood by the customer
and the engineer. In software engineering, we are just beginning to define both what
such parameters ought to be for a software system and-an apparently much harder
task-how to specify them.
Furthermore, in classic engineering disciplines, the engineer is equipped with
tools and the mathematical maturity to specify the properties of the product separately
from those of the design. For example, an electrical engineer relies on mathematical
equations to verify that a design will not violate power requirements. In software
engineering, such mathematical tools are not well developed. The typical software
engineer relies much more on experience and judgment rather than mathematical
techniques. While experience and judgment are necessary, formal analysis tools are
also essential in the practice of engineering.
2.0 Software engineering goals
Stated requirements even for small systems are frequently incomplete when
they are originally specified. Besides accomplishing these stated requirements, a good
software system must be able to easily support changes to these requirements over the
system's life. Thus, a major goal of software engineering is to be able to deal with the
effects of these changes. The most often cited software engineering goals are:
a. Maintainability :It should be possible to easily introduce changes to the
software without increasing the complexity of the original system design.
b. Reliability : The software should prevent failure in design and construction as
well as recover from failure in operation. Put another way, the software should
perform its intended function with the required precision at all times.
c. Efficiency :The software system should use the resources that are available in
an optimal manner.
d. Understandability : The software should accurately model the view the reader
has of the real world. Since code in a large, long-lived software system is usually
read more times than it is written, it should be easy to read at the expense of
being easy to write, and not the other way around.
3.0 Software engineering principles
In order to attain a software system that satisfies the above goals, sound
engineering principles must be applied throughout development, from the design phase
to final fielding of the system. These include:
a. Abstraction : "The essence of abstraction is to extract essential properties while
omitting inessential detail." The software should be organized as a ladder of
abstraction in which each level of abstraction is built from lower levels. The code
is sufficiently conceptual so the user need not have a great deal of technical
background in the subject. The reader should be able to easily follow the logical
path of each of the various modules. The decomposition of the code should be
clear.
b. Information Hiding : The code should contain no unnecessary detail. Elements
that do not affect other portions of the system are inaccessible to the user, so
that only the intended operations can be performed. There are no
"undocumented features".
c. Modularity : The code is purposefully structured. Components of a given
module are logically or functionally dependent.
d. Localization : The breakdown and decomposition of the code is rational.
Logically related computational units are collected together in modules.
e. Uniformity : The notation and use of comments, specific keywords and

formatting is consistent and free from unnecessary differences in other parts of
the code.
f. Completeness : Nothing is blatantly missing from any module. All important or
relevant components are present both in the modules and in the overall system
as appropriate.
g. Confirmability : The modules of the program can be tested individually with
adequate rigor. This gives rise to a more readily alterable system and enables
the reusability of tested components.
4.0 The Role of Software Engineering in System Design
A software system is often a component of a much larger system. The software
engineering activity is therefore a part of a much larger system design activity in which
the requirements of the software are balanced against the requirements of other parts
of the system being designed. For example, a telephone switching system consists of
computers, telephone lines and cables, telephones, perhaps other hardware such as
satellites and finally, software to control the various other components. It is the
combination of all these components that is expected to meet the requirements of the
whole system.
A requirement such as "the system must not be down for more than a second in
20 years" or "when a receiver is taken off the hook, a dial tone is played within half a
second" can be satisfied with a combination of hardware, software and special devices.
The decision of how best to meet the requirement can only be made by considering
many trade-offs. Power plant or traffic monitoring systems, banking systems and
hospital administration systems are other examples of systems that exhibit the need to
view the software as a component of a larger system.
To do software engineering right, then, requires a broader look at the general
problem of system engineering. It requires the software engineer to be involved when
the requirements are being developed initially for the whole system. It requires that the
software engineer attempt to understand the application area rather than just what
abstract interfaces the software must meet. For example, if the system is aimed at
users who are going to use primarily a menu system, it is not wise to develop a
sophisticated word processor as a component of the system.
Above all, any engineering discipline requires the application of compromise- A
classic compromise concerns the choice of what should be done in software and what
should be done in hardware. Software implementation offers flexibility, while hardware
implementation offers performance. For example, a coin-operated machine that could
be built either with several coin slots, one for each type of coin or a single slot, leaving
it to software to recognize the different coins- An even more basic compromise involves
the decision as to what should be automated and what should be done manually.
5.0 A History of Software Engineering

The birth and evolution of software engineering as a discipline within computer
science can be traced to the evolving and maturing view of the programming activity. In
the early days of computing, the problem of programming was viewed essentially as
how to place a sequence of instructions together to get the computer to do something
useful. The problems being programmed were quite well understood-for example, how
to solve a differential equation. The program was written by, say, a physicist to solve an
equation of interest to him or her. The problem was just between the user and the
computer-no other person was involved.
As computers became cheaper and more common, more and more people
started using them. Higher level languages were invented in the late 1950s to make it
easier to communicate with the machine. But still, the activity of getting the computer
to do something useful was essentially done by one person who was writing a program
for a well-defined task.
It was at this time that "programming" attained the status of a profession: you
could ask a programmer to write a program for you instead of doing it yourself- This
introduced a separation between the user and the computer. Now the user had to
specify the task in a form other than the precise programming notation used before.
The programmer then interpreted this specification and translated it into a precise set
of machine instructions. This, of course, sometimes resulted in the programmer
misinterpreting the user's intentions, even in these usually small tasks.
Very few large software projects were being done at this time-the early 1960s-
and these were done by computer pioneers who were experts. For example, the CTSS
operating system developed at MIT was indeed a large project, but it was done by
highly knowledgeable and motivated individuals.
In the middle to late 1960s, truly large software systems were attempted
commercially. The best documented of these projects was the OS 360 operating system
for the IBM 360 computer family. The large projects were the source of the realization
that building large software systems was materially different from building smaller
systems. There were fundamental difficulties in scaling up the techniques of small
program development to large software development. The term "software engineering"
was invented around this time and conferences were held to discuss the difficulties
these projects were facing in delivering the promised products. Large software projects
were universally over budget and behind schedule. Another term invented at this time
was "software crisis."
It was discovered that the problems in building large software systems were not
a matter of putting computer instructions together. Rather, the problems being solved
were not well understood, at least not by everyone involved in the project or by any
single individual. People on the project had to spend a lot of time communicating with
each other rather than writing code. People sometimes even left the project and this
affected not only the work they had been doing but the work of the others who were
depending on them. Replacing an individual required an extensive amount of training
about the "folklore" of the project requirements and the system design. Any change in
the original system requirements seemed to affect many parts of the project, further
delaying system delivery. These kinds of problems just did not exist in the early
"programming" days and seemed to call for a new approach.
Many solutions were proposed and tried. Some suggested that the solution lay
in better management techniques. Others proposed different team organizations. Yet
others argued for better languages and tools. Many called for organization-wide
standards such as uniform coding conventions. There was no shortage of ideas. The
final consensus was that the problem of building software should be approached in the
same way that engineers had built other large complex systems such as bridges,
refineries, factories, ships and airplanes. The point was to view the final software
system as a complex product and the building of it as an engineering job. The
engineering approach required management, organization, tools, theories,
methodologies and techniques. And thus was software engineering born.
The above history emphasizes the growth of software engineering starting from
programming. Other technological trends have also played significant roles in the
evolution of the field. The most important influence has been that of the change in the
balance of hardware and software costs. Whereas the cost of a computerized system
used to be determined largely by hardware costs and software was an insignificant
factor, today the software component can account for far more than half of the system
cost. The declining cost of hardware and the rising cost of software have tipped the
balance further in the direction of software, setting in motion a new economical
emphasis in the development of software engineering.
Another evolutionary trend has been internal to the field itself. There has been a
growing emphasis on viewing software engineering as dealing with more than just
"coding." Instead, the software is viewed as having an entire life cycle, starting from
conception and continuing through design, development, deployment and maintenance
and evolution. The shift of emphasis away from coding has sparked the development of
methodologies and sophisticated tools to support teams involved in the entire software
life cycle.
We can expect the importance of software engineering to continue to grow for
several reasons. First is economics: in 1985, around $140 billion were spent annually
on software worldwide. It is anticipated that software costs will grow to more than -
$450 billion worldwide by 1995. This fact alone ensures that software engineering will
grow as a discipline. Second, software is permeating our society. More and more,
software is used to control critical functions of various machines such as aircrafts and
medical devices. This fact ensures the growing interest of society in dependable
software, to the extent of legislating specific standards, requirements and certification
procedures. No doubt, it will continue to be important to learn how to build better

software better.
6.0 The Role of Software Engineer
The evolution of the field has defined the role of the software engineer and the
required experience and education. A software engineer must of course be a good
programmer, be well-versed in data structures and algorithms and be fluent in
one or more programming languages. These are requirements for "programming-in-
the-small," roughly defined as building programs that can be written in their entirety
by a single individual. But a software engineer is also involved in "programming-in-the-
large," which requires more.
The software engineer must be familiar with several design approaches, be able
to translate vague requirements and desires into precise specifications and be able to
converse with the user of a system in terms of the application rather than in
"computerese.' These in turn require the flexibility and openness to grasp and become
conversant with, the essentials of different application areas. The software engineer
needs the ability to move among several levels of abstraction at different stages of the
project, from specific application procedures and requirements, to abstractions for the
software system, to a specific design for the system and finally to the detailed coding
level.
Modeling is another requirement. The software engineer must be able to build
and use a model of the application to guide choices of the many trade-offs that he or
she will face. The model is used to answer questions about both the behavior of the
system and its performance. Preferably, the model can be used by the engineer as well
as the user. The software engineer is a member of a team and therefore needs
communication skills and interpersonal skills. The software engineer also needs the
ability to schedule work, both of his or her own and that of others.
As discussed above, a software engineer is responsible for many things. In
practice, many organizations divide the responsibilities among several specialists with
different titles. For example, an analyst is responsible for deriving the requirements
and a performance analyst is responsible for analyzing the performance of a system. A
rigid fragmentation of roles, however, is often counterproductive.
7.0 The relationship of Software Engineering to other areas of Computer science
Software engineering has emerged as an important field within computer
science. Indeed, there is a synergistic relationship between it and many other areas in
computer science: these areas both influence and are influenced by software
engineering. In the following subsections, we explore the relationship between software
engineering and some of the other important fields of computer science.
7.1 Programming Languages
The influence of software engineering on programming languages is rather
evident. Programming languages are the central tools used in software development. As
a result, they have a profound influence on how well we can achieve our software
engineering goals. In turn, these goals influence the development of programming
languages.
The most notable example of this influence in recent programming languages is
the inclusion of modularity features, such as separate and independent compilation
and the separation of specification from implementation, in order to support team
development of large software. The Ada programming language, for example, supports
the development of “packages”-allowing the separation of the package interface from its
implementation-and libraries of packages that can be used as components in the
development of independent software systems. This is a step towards making it
possible to build software by choosing from a catalog of available components and
combining them, similarly to the way hardware is built.
In the opposite direction, programming languages have also influenced software
engineering. One example is the idea that requirements and design should be
described precisely, possibly using a language as rigorous and machine-processable as
a programming language. As another example, consider the view that the input to a
software system can be treated as a program coded in some "programming" language.
The commands that a user can type into a system are not just a random collection of
characters; rather they form a language used to communicate with the system.
Designing an appropriate input language is a part of designing the system interface.
Most traditional operating systems, such as OS 360, were developed before this
view was recognized. As a result, the interface to them-the job control language (JCL) is
generally agreed to be extremely complicated to master. On the other hand, more
recent operating systems, such as UNIX, really do provide the user with a
programming language interface, thus making them easier to learn and use.
One result of viewing the software system interface as a programming language
is that compiler development tools-which are quite well developed-can be used for
general software development. For example, we can use grammars to specify the syntax
of the interface and parser-generators to verify the consistency and unambiguity of the
interface and automatically generate the front end for the system.
User interfaces are an especially interesting case because we are now seeing an
influence in the opposite direction. The software engineering issues revolving around
the user interfaces made possible by the widespread use of graphical bit-mapped
displays and mice have motivated programming language work in the area of "visual"
or "pictorial languages. These languages attempt to capture the semantics of the
windowing and interaction paradigms offered by the new sophisticated graphical
display devices.
Yet another influence of the programming language field on software
engineering is through the implementation techniques that have been developed over
the years for language processing. Perhaps the most important lesson learned has been
that formalization leads to automation: stating a formal grammar for a language allows
a parser to be produced automatically. This technique is exploited in many software
engineering areas for formal specification and automatic software generation.
Another implementation influence is due to the two major approaches to
language processing-compilation and interpretation-that have been studied extensively
by compiler designers. In general, the interpretive approach offers more run-time
flexibility and the compilation approach offers more run-time efficiency. Either is
generally applicable to any software system and can therefore become another tool in
the software engineer's toolbox. An example of their application outside the
programming language area can be seen in the data-base field. The queries posed to a
data base may be either compiled or interpreted. Common types of queries are
compiled to ensure their fast execution: the exact search path used by the data-base
system is determined before any queries are made to the system. On the other hand,
since not all types of queries can be predicted by the data-base designer, so-called ad
hoc queries are also supported, which require the data base to select a search path at
run time. The ad hoc queries take longer to execute but give the user more flexibility.
7.2 Operating Systems
The influence of operating systems on software engineering is quite strong
primarily because operating systems were the first really large software systems built,
and therefore they were the first instances of software that needed to be engineered.
Many of the first software design ideas originated from early attempts at building
operating systems.
Virtual machines, levels of abstraction, and separation of policy from
mechanism are all concepts developed in the operating system field with general
applicability to any large software system. For example, the idea of separating a policy
that an operating system wants to impose, such as assuring fairness among jobs, from
the mechanism used to accomplish concurrency, such as time slicing, is an instance of
separating the "what" from the "how"-or the specification from the implementation-and
the changeable parts from what remains fixed in a design. The idea of levels of
abstraction is just another approach to modularizing the design of a system.
In the opposite direction, the influence of software engineering on operating
systems can be seen in both the way operating systems are structured and the goals
they try to satisfy. For example, the UNIX operating system attempts to provide a
productive environment for software development. A class of tools provided in this
environment supports software configuration management-a way to maintain and
control the relationships among the different components and versions of a software
system. Examples of the influence of software engineering techniques on the structure
of operating systems can be seen in portable operating systems and operating systems
that are structured to contain a small "protected" kernel that provides a minimum of
functionality for interfacing with the hardware and a "nonprotected" part that provides
the majority of the functionality previously associated with operating systems. For
example, the nonprotected part may allow the user to control the paging scheme,
which has traditionally been viewed as an integral part of the operating system.
Similarly, in early operating systems, the command language interpreter was an
integral part of the operating system. Today, it is viewed as just another utility
program. This allows, for example, each user to have a personalized version of the
interpreter. On many UNIX systems, there are at least three different such interpreters.
7.3 Data Bases
Data bases represent another class of large software systems whose
development has influenced software engineering through the discovery of new design
techniques. Perhaps the most important influence of the data-base field on software
engineering is through the notion of "data independence," which is yet another
instance of the separation of specification from implementation. The data base allows
applications to be written that use data without worrying about the underlying
representation of the data. This independence allows the data base to be changed in
certain ways-for example, to increase the performance of the system-without any need
to change the applications. This is a perfect example of the benefit of abstraction and
separation of Separation of concerns, two key software engineering principles.
Another interesting impact of data-base technology on software engineering is
that it allows data-base systems to be used as components of large software systems.
Since data bases have solved the many problems associated with the management of
concurrent access to large amounts of information by multiple users, there is no need
to reinvent these solutions when we are building a software system-we can simply use
an existing data-base system as a component.
One interesting influence of software engineering on data-base technology has
its roots in early attempts to use data bases to support software development
environments. This experience showed that traditional data-base technology was
incapable of dealing with the problems posed by software engineering processes. For
example, the following requirements are not handled well by traditional data bases:
storing large structured objects such as source programs or user manuals; storing
large unstructured objects such as object code and executable code; maintaining
different versions of the same object; and storing objects, such as a product, with
many large structured and unstructured fields, such as source code, object code, and
a user manual. Another difficulty dealt with the length of transactions. Traditional data
bases support short transactions, such as a bank account deposit or withdrawal.
Software engineers, on the other hand, need very long transactions: an engineer may
require a long compilation to occur on a multimodule system or may check out a
program and work on it for weeks before checking it back in. The problem posed for the
data base is how to handle the locking of the code during these weeks. What if the
engineer wants to work only on a small part of the program? Are all other access to the
program forbidden?
There is presently considerable work going on in the data-base area to address
such problems, ranging from introducing new models for data bases to adapting
current database models.
7.4 Artificial Intelligence
Artificial intelligence is another field that has exerted influence on software
engineering. The software systems built in the artificial intelligence research
community have been among the most complex systems built. But they have been
different from other software systems in significant ways. Typically, artificial
intelligence systems are built with only a vague notion of how the system is going to
work. The term "exploratory development" has been used for the process followed in
building these systems.
This process is the opposite of traditional software engineering, in which we go
through well-defined steps attempting to produce a complete design before proceeding
to coding. These developments have given rise to new techniques in dealing with
specifications, verification, and reasoning in the presence of uncertainty. Other
techniques advanced by artificial intelligence include the use of logic in both software
specifications and programming languages.
The logic orientation seems to be filling the gap between specification and
implementation by raising the level of implementation languages higher than before.
The logic approach to specification and programming is also called declarative. The
idea is that we declare the specifications or requirements rather than specifying them
procedurally; the declarative description is then executable. Logic programming
languages such as PROLOG help us follow this methodology.
Software engineering techniques have been used in newer artificial intelligence
systems-for example, in expert systems. These systems are modularized, with a clear
separation between the facts "known" by the expert system and the rules used by the
system for processing the facts-for example, a rule to decide on a course of action. This
separation has enabled the building and commercial availability of "expert system
shells" that include the rules only. A user can apply the shell to an application of
interest by supplying application-specific facts. The idea is that the expertise about the
application is provided by the user and the general principles of how to apply expertise
to any problem are provided by the shell.
A different kind of symbiosis is currently taking place at the intersection of
software engineering and artificial intelligence. Techniques of artificial intelligence are
being applied to improve the software engineering tasks. For example, "programming
assistants" are being developed to act as consultants to the programmer, watching for
common programming idioms or the system requirements. Such "assistants" are also
being developed to help in the testing activities of the software development, to debug
the software.
The problem of providing interfaces for nonexpert users-for example, through
the use of natural language-was first attacked by artificial intelligence. Cognitive
models were also used to model the user. These works have influenced the very active
area of user-interface design in software engineering.
7.5 Theoretical Models
Theoretical computer science has developed a number of models that have
become useful tools in software engineering. For example, finite state machines have
served both as the basis of techniques for software specifications and as models for
software design and structure. Communication protocols and language analyzers are
programs that have used finite state machines as their processing model.
Pushdown automata have also been used—for example, for operational
specifications of systems and for building processors for such specifications.
Interestingly, pushdown automata were themselves motivated by practical attempts to
build parsers and compilers for programming languages.
Petri nets are yet another contribution of the theoretical computer science field
to software engineering. While Petri nets were initially used to model hardware
systems, in recent years they have been applied increasingly in the modeling of
software. They are currently the subject of intensive study in many software
engineering research organizations.
As another example, denotational semantics-a mathematical theory developed
for describing programming language semantics-has been the source of recent
developments in the area of specification languages.
Software engineering has also affected theoretical computer science. Algebraic
specifications and abstract data type theory are motivated by the needs of software
engineering. Also in the area of specifications, software engineering has focused more
attention on non-first-order theories of logic, such as temporal logic. Theoreticians
used to pay more attention to first-order theories than higher-order theories because
the two are equivalent in power but first-order theories are more basic from a
mathematical viewpoint. They are not as expressive as higher-order theories, however.
A software engineer, unlike a theoretician, is interested both in the power and the
expressiveness of a theory. For example, temporal logic provides a more compact and
natural style for specifying the requirements of a concurrent system than do first-order
theories. The needs of software engineering, therefore, have ignited new interest by
theoreticians in such higher-order theories.
8.0 The relationship of Software Engineering to Other Disciplines
In the foregoing sections, we examined the relationship between software
engineering and other fields of computer science. In this section, we explore how
software engineering relates to other fields outside of computer science.
Software engineering need not be practiced in a vacuum. There are many problems
that are not specific to software engineering and have been solved in other fields. Their
solutions can be adapted to software engineering. Thus, there is no need to reinvent
every solution. For example, the fields of psychology and industrial design can guide us
in the development of better user interfaces.
8.1 Management Science
A large part of software engineering is involved with management issues. Such
management has two aspects: technical management and personnel management. The
generic issues in any kind of project management include project estimation, project
scheduling, human resource planning, task decomposition and assignment, and
project tracking. The personnel issues involve hiring personnel, motivating people and
assigning the right people to the right tasks.
Management science studies exactly these issues. Many models have been
developed that can be applied in software engineering. By looking to management
science, we can exploit the results of many decades of study.
In the opposite direction, software engineering has provided management
science with a new domain in which to test management theories and models. The
traditional management approaches to assembly-line production clearly do not apply to
software engineering management, giving rise to a search for more applicable
approaches.
8.2 Systems Engineering
Systems engineering is the field concerned with studying complex systems. The
hypothesis is that certain laws govern the behavior of any complex system composed
many components with complex relationships. Systems engineering is useful when you
are interested in concentrating on the system as opposed to the individual
components. Systems engineering tries to discover common themes that apply to
diverse systems-for example, chemical plants, buildings and bridges.
Software is often a component of a much larger system. For example, the
software in a factory monitoring system or the flight software on an airplane is just
components of more complicated systems. Systems engineering techniques can be
applied to the study of such systems. We can also consider a software system
consisting of thousands of modules as a candidate complex system subject to systems
engineering laws. On the other hand, system engineering has been enriched by
expanding its set of analysis models-which were traditionally based on classical
mathematics-to include discrete models that have been in use in software engineering.
9.0 Summary:
In this lesson we have discussed the meaning of Software engineering, its
history, role and importance and the Software life cycle. We have also discussed the
relationship of Software Engineering to other areas of Computer science

Q1: What is Software Engineering? What is the role of Software Engineering in
System Design?
Q2: Explain the various goals and principles of Software Engineering.
Q3: Write a detailed note on the history of Software Engineering.
Q4: Explain in detail the relationship of Software Engineering to other areas of
Computer science.
“Software Engineering: A Practitioner’s Approach” by Roger Pressman, Tata McGraw
Hill Publications.
“Software Engineering” by David Gustafson, Schaum’s Outlines Series.
“An Integrated Approach to Software Engineering” by Pankaj Jalote.
“Software Engineering” by Ian Sommerville, Pearson Education, Asia.
“Software Engineering Concepts” by Richard Fairley, Tata McGraw Hill Publications.
“Fundamentals of software engineering”by Carlo Ghezi, Mehdi Jazayeri.
“Software engineering: Theory and practice” by Shari Lawrence Pfleeger.
“Fundamentals of Software Engineering” by Rajib Mall., PHI-India.
“Software Engineering” by K.K. Aggarwal, Yogesh Singh, New Age International
Publishers.
M.Sc (IT) PART-II PAPER: MS(A)-213
The Software Life Cycle
1.0 The Software Life Cycle

2.0 Classification of Software Qualities
2.1 External Versus Internal Qualities
2.2 Product And Process Qualities
3.0 Representative Qualities
3.1 Correctness, Reliability, And Robustness
3.2 Performance
3.3 User Friendliness
3.4 Verifiability
3.5 Maintainability
3.6 Reusability
3.7 Portability
3.8 Understandability
3.9 Interoperability
3.10 Productivity
3.11 Timeliness
3.12 Visibility
4.0 Summary
Objectives:
In this lesson we will study the software life cycle, which is a very important
concept in the field of software engineering. We will study in detail the classification of
software qualities and the representative qualities of the software.
1.0 The Software Life Cycle
From the inception of an idea for a software system, until it is implemented and
delivered to a customer and even after that, the system undergoes gradual
development and evolution. The software is said to have a life cycle composed of several
phases. Each of these phases results in the development of either a part of the system
or something associated with the system, such as a test plan or a user manual. In the
traditional life cycle model also called the "waterfall model," each phase has well-
defined starting and ending points, with clearly identifiable deliverables to the next
phase. In practice, it is rarely so simple. A sample waterfall life cycle model comprises
the following phases.

Requirements analysis and specification: Requirements analysis is usually the first
phase of a large-scale software development project. It is undertaken after a feasibility
study has been performed to define the precise costs and benefits of a software system.
The purpose of this phase is to identify and document the exact requirements for the
system. Such study may be performed by the customer, the developer, a marketing
organization, or any combination of the three. In cases where the requirements are not
clear-e.g., for a system that has never been done before-much interaction is required
between the user and the developer. The requirements at this stage are in end-user
terms. Various software engineering methodologies advocate that this phase must also
produce user manuals and system test plans.
Design and specification: Once the requirements for a system have been documented,
software engineers design a software system to meet them. This phase is sometimes
split into two subphases: architectural or high-level design and detailed design. High-
level design deals with the overall module structure and organization, rather than the
details of the modules. The high-level design is refined by designing each module in
detail (detailed design).
Separating the requirements analysis phase from the design phase is an
instance of a fundamental "what/how" dichotomy that we encounter quite often in
computer science. The general principle involves making a clear distinction between
what the problem is and how to solve the problem. In this case, the requirements
phase attempts to specify what the problem is. There are usually many ways that the
requirements may be met, including some solutions that do not involve the use of
computers at all. The purpose of the design phase is to specify a particular software
system that will meet the stated requirements. Again, there are usually many ways to
build the specified system. In the coding phase, which follows the design phase, a
particular system is coded to meet the design specification.
Coding and module testing: This is the phase that produces the actual code that will
be delivered to the customer as the running system. The other Phases of the life cycle
may also develop code, such as prototypes tests, and test drivers, but these are for use
by the developer. Individual modules develops this phase are also tested before being
delivered to the next phase.
Integration and system testing: All the modules that have been developed before and
tested individually are put together integrated-in this phase and tested as a whole system.
Delivery and maintenance: Once the system passes all the tests, it is delivered to the
customer and enters the maintenance phase. Any modifications made to the system after
initial delivery is usually attributed to this phase.
Figure 1.1 gives a graphical view of the software development life cycle that provides a
visual explanation of the term "waterfall" used to denote it. Each phase results that
"flow" into the next and the process ideally proceeds in an orderly and linear fashion.
A commonly used terminology distinguishes between high phases and low
phases of the software life cycle; the feasibility study, requirements analysis and high
level design contribute to the former/and implementation-oriented activities contribute
to the latter.
As presented here, the phases give a partial, simplified view of the conventional
waterfall software life cycle. The process may be decomposed into a different set of
phases, with different names, different purposes and different granularity. Entirely
different life cycle schemes may even be proposed, not based on a strictly phased
waterfall development. For example, it is clear that if any tests uncover defects in the
system, we have to go back at least to the coding phase and perhaps to the design
phase to correct some mistakes. In general, any phase may uncover problems in
previous phases; this will necessitate going back to the previous phases and redoing
some earlier work. For example, if the system design phase uncovers inconsistencies or
ambiguities in the system requirements, the requirements analysis phase must be
revisited to determine what requirements were really intended.
Another simplification in the above presentation is that it assumes that a phase
is completed before the next one begins. In practice, it is often expedient to start a
phase before a previous one is finished. This may happen, for example, if some data
necessary for the completion of the requirements phase will not be available for some
time. Or it might be necessary because the people ready to start the next phase are
available and have nothing else to do. The research and experience over the past
decade have shown that there is a variety of life cycle models and that no single one is
appropriate for all software systems.
The nature and qualities of Software
The goal of any engineering activity is to build something-a product. The civil
engineer builds a bridge, the aerospace engineer builds an airplane and the electrical
engineer builds a circuit. The product of software engineering is a “software system”. It is
not as tangible as the other products, but it is a product nonetheless. It serves a function.
In some ways software products are similar to other engineering Products and in some
ways they are very different. The characteristic that perhaps set software apart from other
engineering products the most is that software is malleable. We can modify the product
itself-as opposed to its design-rather easily. This makes software quite different from
other products such as cars or ovens.
The malleability of software is often misused. While it is certainly possible to
modify a bridge or an airplane to satisfy some new need-for example to make the bridge
support more traffic or the airplane carry more cargo-such a modification is not taken
lightly and certainly is not attempted without first making a design change and verifying the
impact of the change extensively. Software engineers, on the other hand, are often asked to
perform such modifications on software. Because of its malleability, we seem to think that
changing software is easy. In practice, it is not.
We may be able to change the code easily with a text editor, but meeting the need
for which the change was intended is not necessarily done so easily- Indeed we need to treat
software like other engineering products in this regard: a change in software must be viewed
as a change in the design rather than in the code, which is just an instance of the product.
We can indeed exploit the malleability property, but we need to do it with discipline.
Another characteristic of software is that its creation is human intensive: it
requires mostly engineering rather than manufacturing. In most other engineering
disciplines, the manufacturing process is considered carefully because it determines
the final cost of the product. Also, the process has to be managed closely to ensure
that defects are not introduced. The same considerations apply to computer hardware
products. For software, on the other hand, "manufacturing" is a trivial process of
duplication. The software production process deals with design and implementation,
rather than manufacturing. This process has to meet certain criteria to ensure the
production of high-quality software.
Any product is expected to fulfill some need and meet some acceptance
standards that set forth the qualities it must have. A bridge performs the function of
making it easier to travel from one point to another; one of the qualities it is expected
to have is that it will not collapse when the first strong wind blows or a convoy of
trucks travels across it. In traditional engineering disciplines, the engineer has tools for
describing the qualities of the product distinctly from the design of the product. In
software engineering, the distinction is not yet so clear. The qualities of the software
product are often intermixed in specifications with the qualities of the design.
In this lesson, we examine the qualities that are pertinent to software products and
software production processes.
2.0 Classification of Software Qualities
There are many desirable software qualities. Some of these apply both to the
product and to the process used to produce the product. The user wants the software
product to be reliable, efficient and easy to use. The producer of the software wants it
to be verifiable, maintainable, portable and extensible. The manager of the software
project wants the process of software development to be productive and easy to control.
In this section, we consider two different classifications of software-related qualities:
internal versus external and product versus process.
2.1 External versus Internal Qualities
We can divide software qualities into external and internal qualities. The
external Qualities are visible to the user of the system and the internal qualities are
those that concerns the developer of the system. In general the users of the software
only care about the external qualities, but it is the internal qualities-which deal largely
with the structure of the software-that help developers achieve the external qualities.
For example, the internal quality of verifiability is necessary for achieving the external
quality of reliability. In many cases, however, the qualities are related closely and the
distinction between internal and external is not sharp.
2.2 Product and Process Qualities

We use a process to produce the software product. We can also attribute some
qualities to the process, although process qualities often are closely related to product
qualities. For example, if the process requires careful planning of system test data
before any design and development of the system starts, product reliability will
increase. Some qualities such as efficiency, apply both to the product and to the
process.
It is interesting to examine the word product here. It usually refers to what is
delivered to the customer. Even though this is an acceptable definition from the
customer's perspective, it is not adequate for the developer who requires a general
definition of a software product that encompasses not only the object code and the
user manual that are delivered to the customer, but also the requirements, design,
source code, test data, etc. With such a definition, all of the artifacts that are produced
during the process constitute parts of the product. In fact, it is possible to deliver
different subsets the same product to different customers.
For example, a computer manufacturer might sell to a process control company
the object code to be installed in the specialized hardware for an embedded application.
It might sell the object code and the user's manual to software dealers. It might even
sell the design and the source code to software vendors who modify them to build other
products. In this case, the developers of the original system see one product, the
salespersons in the same company see a set of related products and the end user and
the software vendor see still other, different products.
3.0 Representative Qualities
In this section, we present the most important qualities of software products
and processes. Where appropriate, we analyze a quality with respect to the
classifications discussed above.
3.1 Correctness, Reliability, and Robustness
The terms "correctness," "reliability," and "robustness" are often used
interchangeably to characterize a quality of software that implies that the application
performs its functions as expected. At other times, the terms are used with different
meanings by different people, but the terminology is not standardized. This is quite
unfortunate, because these terms deal with important issues. We will try to clarify
these issues below, not only because we need a uniform terminology to be used
throughout the book, but also because we believe that a clarification of the terminology
is needed to better understand and analyze the underlying issues.
3.1.1 Correctness
A program is functionally correct if it behaves according to the specification of
the functions it should provide (called functional requirements specifications). It is
common simply to use the term "correct" rather than "functionally correct"; similarly,
in this context, the term "specifications" implies "functional requirements
specifications." We will follow this convention when the context is clear.

The definition of correctness assumes that a specification of the system is
available and that it is possible to determine unambiguously whether or not a program
meets the specifications. With most current software systems, no such specification
exists. If a specification does exist, it is usually written in an informal style using
natural language. Such a specification is likely to contain many ambiguities.
Regardless of these difficulties with current specifications, however, the definition of
correctness is useful. Clearly, correctness is a desirable property for software systems.
Correctness is a mathematical property that establishes the equivalence
between the software and its specification. Obviously, we can be more systematic and
precise in assessing correctness depending on how rigorous we are in specifying
functional requirements. Correctness can be assessed through a variety of methods,
some stressing an experimental approach (e.g., testing), others stressing an analytic
approach (e.g.. formal verification of correctness). Correctness can also be enhanced by
using appropriate tools such as high-level languages, particularly those supporting
extensive static analysis. Likewise, it can be improved by using standard algorithms or
using libraries of standard modules, rather than inventing new ones.
3.1.2 Reliability
Informally, software is reliable if the user can depend on it. The specialized
literature on software reliability defines reliability in terms of statistical behavior-the
probability that the software will operate as expected over a specified time interval.
Correctness is an absolute quality: any deviation from the requirements makes
the system incorrect, regardless of how minor or serious is the consequence of the
deviation. The notion of reliability is on the other hand, relative: if the consequence of a
software error is not serious, the incorrect software may still be reliable.
Engineering products are expected to be reliable. Unreliable products, in
general, disappear quickly from the marketplace. Unfortunately, software products
have not achieved this enviable status yet. Software products are commonly released
along with a list of "Known Bugs." Users of software take it for granted that "Release 1"
of a product is "buggy." This is one of the most striking symptoms of the immaturity of
the software engineering field as an engineering discipline.
In classic engineering disciplines, a product is not released if it has "bugs." You
do not expect to take delivery of an automobile along with a list of shortcomings or a
bridge with a warning not to use the railing. Design errors are extremely rare and
worthy of news headlines. A bridge that collapses may even cause the designers to be
prosecuted in court.
On the contrary, software design errors are generally treated as unavoidable.
Far from being surprised with the occurrence of software errors, we expect them.
Whereas with all other products the customer receives a guarantee of reliability, with
software we get a disclaimer that the software manufacturer is not responsible for any
damages due product errors. Software engineering can truly be called an engineering
discipline only when we can achieve software reliability comparable to the reliability of
other products.
Figure 2.1 Relationship between correctness and reliability in the ideal case.
3.1.3 Robustness
A program is robust if it behaves "reasonably," even in circumstances that were
not anticipated in the requirements specification-for example, when it encounters
incorrect input data or some hardware malfunction (say, a disk crash). A program that
assumes perfect input and generates an unrecoverable run-time error as soon as the
user inadvertently types an incorrect command would not be robust. It might be
correct, though, if the requirements specification does not state what the action should
be upon entry of an incorrect command. Obviously, robustness is a difficult-to-define
quality; after all, if we could state precisely what we should do to make an application
robust, we would be able to specify -its "reasonable" behavior completely. Thus,
robustness would become equivalent to correctness (or reliability, in the sense of
Figure 2.1).
3.2 Performance
Any engineering product is expected to meet a certain level of performance.
Unlike other disciplines, in software engineering we often equate performance with
efficiency. We will follow this practice here. A software system is efficient if it uses
computing resources economically.
Performance is important because it affects the usability of the system. If a
software system is too slow, it reduces the productivity of the users, possibly to the
point of not meeting their needs. If a software system uses too much disk space, it may
be too expensive to run. If a software system uses too much memory, it may affect the
other applications that are run on the same system, or it may run slowly while the
operating system tries to balance the memory usage of the different applications.
Underlying all of these statements-and also what makes the efficiency issue
difficult-are the changing limits of efficiency as technology changes. Our view of what is
"too expensive" is constantly changing as advances in technology extend the limits. The
computers of today cost orders of magnitude less than computers of a few years ago,
yet they provide orders of magnitude more power.
Performance is also important because it affects the scalability of a software
system. An algorithm that is quadratic may work on small inputs but not work at all
for larger inputs. So, there are several ways to evaluate the performance of a system.
One method is to measure efficiency by analyzing the complexity of algorithms. An
extensive theory exists for characterizing the average or worst case behavior of
algorithms, in terms of significant resource requirements such as time and space, or-
less traditionally-in terms of number of message exchanges (in the case of distributed
systems).
Analysis of the complexity of algorithms provides only average or worst case
information, rather than specific information, about a particular implementation. For
more specific information, we can use techniques of performance evaluation. The three
basic approaches to evaluating the performance of a system are measurement,
analysis, and simulation. We can measure the actual performance of a system by
means of monitors that collect data while the system is working and allow us to
discover bottlenecks in the system. Or we can build a model of the product and analyze
it. Or, finally, we can even build a model that simulates the product. Analytic models-
often based on queuing theory-are usually easier to build but are less accurate while
simulation models are more costly to build but are more accurate. We can combine the
two techniques as follows: at the start of a large project, an analytic model can provide
a general understanding of the performance-critical areas of the product, pointing out
areas where more thorough study is required; then we can build simulation models of
these particular areas.
3.3 User Friendliness
A software system is user friendly if its human users find it easy to use. This
definition reflects the subjective nature of user friendliness. An application that is used
by novice programmers qualifies as user friendly by virtue of different properties than
an application that is used by expert programmers. For example, a novice user may
appreciate verbose messages, while an experienced user grows to detest and ignore
them. Similarly, a nonprogrammer may appreciate the use of menus, while a
programmer may be more comfortable with typing a command.
The user interface is an important component of user friendliness. A software
system that presents the novice user with a window interface and a mouse is friendlier
than one that requires the user to use a set of one-letter commands. On the other
hand, an experienced user might prefer a set of commands that minimize the number
of keystrokes rather than a fancy window interface through which he has to navigate to
get to the command that he knew all along he wanted to execute.
There is more to user friendliness, however, than the user interface. For
example, an embedded software system does not have a human user interface. Instead,
it interacts with hardware and perhaps other software systems. In this case, the user
friendliness is reflected in the ease with which the system can be configured and
adapted to the hardware environment.
In general, the user friendliness of a system depends on the consistency of its

user and operator interfaces. Clearly, however, the other qualities mentioned above-
such as correctness and performance-also affect user friendliness. A software system
that produces wrong answers is not friendly, regardless of how fancy its user interface
is. Also, a software system that produces answers more slowly than the user requires
is not friendly even if the answers are displayed in color.
3.4 Verifiability
A software is verifiable if its properties can be verified easily. For example, the
correctness or the performances of a software system are properties we would be
interested in verifying. Verification can be performed either by formal analysis methods
or through testing. A common technique for improving verifiability is the use of
"software monitors," that is, code inserted in the software to monitor various qualities
such as performance or correctness.
Modular design, disciplined coding practices and the use of an appropriate
programming language all contribute to verifiability.
Verifiability is usually an internal quality, although it sometimes becomes an
external quality also. For example, in many security critical applications, the customer
requires the verifiability of certain properties. The highest level of the security standard
for a “trusted computer system” requires the verifiability of the operating system
kernel.
3.5 Maintainability
The term “software maintenance” is commonly used to refer to the modifications
that are made to a software system after its initial release. Maintenance used to be
viewed as merely “bug fixing”, and it was distressing to discover that so much effort
was spent on fixing defects. Studies have shown, however, that the majority of time
spent on maintenance is in fact spent on enhancing the product with features that
were not in the original specifications or were stated incorrectly there.
There is evidence that maintenance costs exceed 60% of the total costs of the
software. To analyze the factors that affect such costs, it is customary to divide
software maintenance into three categories: corrective, adaptive and perfective
maintenance. Corrective maintenance has to do with the removal of residual errors
present in the product when it is delivered as well as errors introduced into the
software during its maintenance. Corrective maintenance accounts for about 20
percent of maintenance costs.
Adaptive and perfective maintenance are the real sources of change in software;
they motivate the introduction of evolvability as a fundamental software quality and
anticipation of change as a general principle that should guide the software engineer.
Adaptive maintenance accounts for nearly another 20 percent of maintenance costs
while over 50 percent is absorbed by perfective maintenance.
Adaptive maintenance involves adjusting the application to changes in the
environment, e.g., a new release of the hardware or the operating system or a new
database system. In other words, in adaptive maintenance the need for software
changes cannot be attributed to a feature in the software itself, such as the presence of
residual errors or the inability to provide some functionality required by the user.
Rather, the software must change because the environment in which it is embedded
changes.
Finally, perfective maintenance involves changing the software to improve some
of its qualities. Here, changes are due to the need to modify the functions offered by the
application, add new functions, improve the performance of the application, make it
easier to use, etc. The requests to perform perfective maintenance may come directly
from the software engineer, in order to improve the status of the product on the
market, or they may come from the customer, to meet some new requirements.
3.5.1 Repairability
A software system is repairable if it allows the correction of its defects with a
limited amount of work. In many engineering products, repairability is a major design
goal. For example, automobile engines are built with the parts that are most likely to
fail as the most accessible. In computer hardware engineering, there is a subspecialty
called repairability, availability, and serviceability (RAS).
An analogous situation applies to software: a software product that consists of
well-designed modules is much easier to analyze and repair than a monolithic one.
Merely increasing the number of modules, however, does not make a more repairable
product. We have to choose the right module structure with the right module interfaces
to reduce the need for module interconnections. The right modularization promotes
repairability by allowing errors to be confined to few modules, making it easier to locate
and remove them. Repairability can be improved through the use of proper tools. For
example, using a high-level language rather than an assembly language leads to better
repairability. Also, tools such as debuggers can help in isolating and repairing errors.
3.5.2 Evolvability
Like other engineering products, software products are modified over time to
provide new functions or to change existing functions. Indeed, the fact that software is
so malleable makes modifications extremely easy to apply to an implementation. There
is, however, a major difference between software modification and modification of other
engineering products. In the case of other engineering products, modifications start at
the design level and then proceed to the implementation of the product. For example, if
one decides to add a second story to a house, first one must do a feasibility study to
check whether this can be done safely. Then one is required to do a design, based on
the original design of the house. Then the design must be approved, after assessing
that it does not violate the existing regulations. And, finally, the construction of the
new part may be commissioned.
In the case of software, unfortunately, people seldom proceed in such an
organized fashion. Although the change might be a radical change in the application,
too often the implementation is started without doing any feasibility study, let alone a
change in the original design. Still worse, after the change is accomplished, the
modification is not even documented a posteriori; i.e., the specifications are not
updated to reflect the change. This makes future changes more and more difficult to
apply.
On the other hand, successful software products are quite long lived. Their first
release is the beginning of a long lifetime and each successive release is the next step
in the evolution of the system. If the software is designed with care and if each
modification is thought out carefully, then it can evolve gracefully.
As the cost of software production and the complexity of applications grow, the
evolvability of software assumes more and more importance. One reason for this is the
need to leverage the investment made in the software as the hardware technology
advances. Some of the earliest large systems developed in the 1960s are today taking
advantage of new hardware, device and network technologies.
Most software systems start out being evolvable, but after years of evolution they
reach a state where any major modification runs the risk of "breaking" existing
features. In fact, evolvability is achieved by modularization and successive changes
tend to reduce the modularity of the original system. This is even worse if modifications
are applied without careful study of the original design and without precise description
of changes in both the design and the requirements specification.
Indeed, studies of large software systems show that evolvability decreases with
each release of a software product. Each release complicates the structure of the
software, so that future modifications become more difficult. To overcome this problem,
the initial design of the product, as well as any succeeding changes, must be done with
evolvability in mind. Evolvability is one of the most important software qualities, and
the principles. Evolvability is both a product- and process-related quality. In terms of
the latter, the process must be able to accommodate new management and
organizational techniques. changes in engineering education, etc.
3.6 Reusability
Reusability is an important factor. In product evolution, we modify a product to
build a new version of that same product; in. product reuse, we use it-perhaps with
minor changes-to build another product. Reusability appears to be more applicable to
software components than to whole products but it certainly seems possible to build
products that are reusable.
A good example of a reusable product is the UNIX shell. The UNIX shell is a
command language interpreter; that is, it accepts user commands and executes them.
But it is designed to be used both interactively and in "batch." The ability to start a
new shell with a file containing a list of shell commands allows us to write programs-
scripts-in the shell command language. We can view the program as a new product
that uses the shell as a component. By encouraging standard interfaces, the UNIX
environment in fact supports the reuse of any of its commands, as well as the shell, in
building powerful utilities.
Scientific libraries are the best known reusable components. Several large
FORTRAN libraries have existed for many years. Users can buy these and use them to
build their own products, without having to reinvent or recode well-known algorithms.
Indeed, several companies are devoted to producing just suchlibraries.
3.7 Portability
Software is portable if it can run in different environments. The term
"environment" can refer to a hardware platform or a software environment such as a
particular operating system. With the proliferation of different processors and
operating systems, portability has become an important issue for software engineers.
Even within one processor family, portability can be important because of the
variations in memory capacity and additional instructions. One way to achieve
portability within one machine architecture is to have the software system assume a
minimum configuration as far as memory capacity is concerned and use a subset of
the machine facilities that are guaranteed to be available on all models of the
architecture (such as machine instructions and operating system facilities). But this
penalizes the larger models because, presumably, the system can perform better on
these models if it does not make such restrictive assumptions. Accordingly, we need to
use techniques that allow the software to determine the capabilities of the hardware
and to adapt to them. One good example of this approach is the way that UNIX allows
programs to interact with many different terminals without explicit assumptions in the
programs about the terminals they are using. The X Windows system extends this
capability to allow applications to run on any bit-mapped display.
More generally, portability refers to the ability to run a system on different hardware
platforms. As the ratio of money spent on software versus hardware increases,
portability gains more importance.
3.8 Understandability
Some software systems are easier to understand than others. Of course, some
tasks are inherently more complex than others. For example, a system that does
weather forecasting, no matter how well it is written, will be harder to understand than
one that prints a mailing list. Given tasks of inherently similar difficulty, we can follow
certain guidelines to produce more understandable designs and to write more
understandable programs.
Understandability is an internal product quality, and it helps in achieving many
of the other qualities such as evolvability and verifiability. From an external point of
view the user considers the system understandable if it has predictable behavior.
External understandability is a component of user friendliness.
3.9 Interoperability
Interoperability refers to the ability of a system to coexist and cooperate with
other systems-for example, a word-processor's ability to incorporate a chart produced
by a graphing package or the graphics package's ability to graph the data produced by
a spreadsheet, or the spreadsheet's ability to process an image scanned by a scanner.
While rare in software products, interoperability abounds in other engineering
products. For example, stereo systems from various manufacturers work together and
can be connected to television sets and recorders. In fact, stereo systems produced
decades ago accommodate new technologies such as compact discs, while virtually
every operating system has to be modified-sometimes significantly before it can work
with the new optical disks.
Once again, the UNIX environment, with its standard interfaces, offers a limited
example of interoperability within a single environment: UNIX encourages applications
to have a simple, standard interface, which allows the output of one application to be
used as the input to another.
3.10 Productivity
Productivity is a quality of the software production process: it measures the
efficiency of the process and as we said before, is the performance quality applied to
the process. An efficient process results in faster delivery of the product.
Individual engineers produce software at a certain rate, although there are great
variations among individuals of different ability. When individuals are part of a team,
the productivity of the team is some function of the productivity of the individuals. Very
often, the combined productivity is much less than the sum of the parts. Process
organizations and techniques are attempts at capitalizing on the individual
productivity of team members.
Productivity offers many trade-offs in the choice of a process. For example, a
process that requires specialization of individual team members may lead to
productivity in producing a certain product, but not in producing a variety of products.
Software reuse is a technique that leads to the overall productivity of an organization
that is involved in developing many products, but developing reusable modules is
harder than developing modules for one's own use. thus reducing the productivity of
the group that is developing reusable modules as part of their product development.
3.11 Timeliness
Timeliness is a process-related quality that refers to the ability to deliver a
product on time. Historically, timeliness has been lacking in software production
processes leading to the "software crisis," which in turn led to the need for-and birth of
software engineering itself. Even now, many current processes fail to result in a timely
product.
Timeliness by itself is not a useful quality, although being late may preclude
market opportunities. Delivering on time a product that is lacking in other qualities,
such as reliability or performance is pointless.

Timeliness requires careful scheduling, accurate work estimation and clearly
specified and verifiable milestones. All other engineering disciplines use standard
project management techniques to achieve timeliness. There are even many computer
supported project management tools.
Standard project management techniques are difficult to apply in software
engineering because of the difficulty in measuring the amount of work required for
producing a given piece of software, the difficulty in measuring the productivity of
engineers or even having a dependable metric for productivity and the use of imprecise
and unverifiable milestones. Another reason for the difficulty in achieving timeliness in
the software process is continuously changing user requirements.
One technique for achieving timeliness is through incremental delivery of the
product. Incremental delivery allows the product to become available earlier; and the
use of the product helps in refining the requirements incrementally. Outside of
software engineering, a classic example of the difficulty in dealing with the
requirements of complex systems is offered by modern weapons systems. In several
well-publicized cases, the weapons have been obsolete by the time they have been
delivered, or they have not met the requirements or in many cases, both. But after ten
years of development, it is difficult to decide what to do with a product that does not
meet a requirement stated ten years ago. The problem is exacerbated by the fact that
requirements cannot be formulated precisely in these cases because the need is for the
most advanced system possible at the time of delivery, not at the time the requirements
are defined.
Obviously, incremental delivery depends on the ability to break down the set of
required system functions into subsets that can be delivered in increments. If such
subsets cannot be defined, no process can make the product available incrementally.
But a non-incremental process prevents the production of product subsets even if such
subsets can be identified. Timeliness can be achieved by a product that can be broken
down into subsets and an incremental process.
3.12 Visibility
A software development process is visible if all of its steps and its current status
are documented clearly. The idea is that the steps and the status of the project are
available and easily accessible for external examination. In many software projects,
most engineers and even managers are unaware of the exact status of the project.
Some may be designing, others coding and still others testing, all at the same time.
This, by itself, is not bad. Yet, if an engineer starts to redesign a major part of the code
just before the software is supposed to be delivered for integration testing, the risk of
serious problems and delays will be high.
Visibility allows engineers to weigh the impact of their actions and thus guides
them in making decisions. It allows the members of the team to work in the same
direction, rather than, as are often the case currently, in cross directions. The most
common example of the latter situation is as mentioned above, when the integration
test group has been testing a version of the software assuming that the next version
will involve fixing defects and will be only minimally different from the current version,
while an engineer decides to do a major redesign to correct a minor defect. The tension
between one group trying to stabilize the software while another person or group is
destabilizing lt-unintentionally, of course-is common. The process must encourage a
consistent view of the status and current goals among all participants.
Visibility is not only an internal quality; it is also external. During the course of
a long project, there are many requests about the status of the project. Sometimes
these require formal presentations on the status and at other times the requests are
informal. Sometimes the requests come from the organization's management for future
planning, and at other times they come from the outside, perhaps from the customer. If
the software development process has low visibility, either these status reports will not
be accurate, or they will require a lot of effort to prepare each time.
So, visibility of the process requires not only that all process steps be
documented, but also that the current status of the intermediate products, such as
requirements specifications and design specifications, be maintained accurately; that
is, visibility of the product is required also. Intuitively, a product is visible if it is clearly
structured as a collection of modules, with clearly understandable functions and easily
accessible documentation.
4.0 Summary
In this lesson we have studied the software life cycle, which is a very important
concept in the field of software engineering. We have also studied the classification of
software qualities and the representative qualities of the software.
5.0 Self Check Exercise
Q1: Explain in detail the software life cycle.
Q2: What are the various classifications of Software Qualities?
Q3: What are the various representative qualities of the software?
Hill Publications.
“Software Engineering” by K.K. Aggarwal, Yogesh Singh, New Age International Publishers.
M.Sc (IT) PART-II PAPER: MS(A)-213
Software Process Models

1.0 Introduction
2.0 Waterfall model
3.0 Spiral model
4.0 Prototyping model
5.0 Tools and techniques for process modeling
6.0 Summary
Objectives:
In this lesson we will study the various process models such as Waterfall model,
Spiral model, Prototyping model etc.
1.0 Introduction: SDLC Models
There are various software development approaches defined and designed which
are used/employed during development process of software, these approaches are also
referred as "Software Development Process Models".
Each process model follows a particular life cycle in order to ensure success in
process of software development.
Some of the most commonly used Software developments models are the following:
 Waterfall model
 Spiral model
 Prototyping model
 Iterative model
 RAD model
2.0 Waterfall Model
Waterfall approach was first Process Model to be introduced and followed
widely in Software Engineering to ensure success of the project. In "The Waterfall"
approach, the whole process of software development is divided into separate process
phases. The phases in Waterfall model are: Requirement Specifications phase, Software
Design, Implementation and Testing & Maintenance. All these phases are cascaded to
each other so that second phase is started as and when defined set of goals are
achieved for first phase and it is signed off, so the name "Waterfall Model". All the
methods and processes undertaken in Waterfall Model are more visible. It refers to a
software development model in which phases are performed sequentially with little or
no overlap.
The stages of "The Waterfall Model" are:

Requirement Analysis & Definition: All possible requirements of the system to be
developed are captured in this phase. Requirements are set of functionalities and
constraints that the end-user (who will be using the system) expects from the system.
The requirements are gathered from the end-user by consultation, these requirements
are analyzed for their validity and the possibility of incorporating the requirements in
the system to be development is also studied. Finally, a Requirement Specification
document is created which serves the purpose of guideline for the next phase of the
model.
System & Software Design: Before a starting for actual coding, it is highly important
to understand what we are going to create and what it should look like? The
requirement specifications from first phase are studied in this phase and system
design is prepared. System Design helps in specifying hardware and system
requirements and also helps in defining overall system architecture. The system design
specifications serve as input for the next phase of the model.
Implementation & Unit Testing: On receiving system design documents, the work is
divided in modules/units and actual coding is started. The system is first developed in
small programs called units, which are integrated in the next phase. Each unit is
developed and tested for its functionality; this is referred to as Unit Testing. Unit
testing mainly verifies if the modules/units meet their specifications.
Integration & System Testing: As specified above, the system is first divided in units
which are developed and tested for their functionalities. These units are integrated into
a complete system during Integration phase and tested to check if all modules/units
coordinate between each other and the system as a whole behaves as per the
specifications. After successfully testing the software, it is delivered to the customer.
Operations & Maintenance: This phase of "The Waterfall Model" is virtually never
ending phase (Very long). Generally, problems with the system developed (which are
not found during the development life cycle) come up after its practical use starts, so
the issues related to the system are solved after deployment of the system. Not all the
problems come in picture directly but they arise time to time and needs to be solved;
hence this process is referred as Maintenance.
Advantages of waterfall model
The advantage of waterfall development is that it allows for departmentalization
and managerial control. A schedule can be set with deadlines for each stage of
development and a product can proceed through the development process like a car in
a carwash, and theoretically, be delivered on time. Development moves from concept,
through design, implementation, testing, installation, troubleshooting and ends up at
operation and maintenance. Each phase of development proceeds in strict order,
without any overlapping or iterative steps.
Disadvantages of waterfall model
The disadvantage of waterfall development is that it does not allow for much
reflection or revision. Once an application is in the testing stage, it is very difficult to go
back and change something that was not well-thought out in the concept stage.
Alternatives to the waterfall model include joint application development (JAD), rapid
application development (RAD), synch and stabilize, build and fix and the spiral model.
Waterfall Model Common Error
Common Errors in Requirements Analysis
In the traditional waterfall model of software development, the first phase of
requirements analysis is also the most important one. This is the phase which involves
gathering information about the customer's needs and defining, in the clearest possible
terms, the problem that the product is expected to solve. This analysis includes
understanding the customer's business context and constraints, the functions the
product must perform, the performance levels it must adhere to and the external
systems it must be compatible with. Techniques used to obtain this understanding
include customer interviews, use cases, and "shopping lists" of software features. The
results of the analysis are typically captured in a formal requirements specification,
which serves as input to the next step. Well, at least that's the way it's supposed to
work theoretically. In reality, there are a number of problems with this theoretical
model and these can cause delays and knock-on errors in the rest of the process. This
part discusses some of the more common problems that project managers
Experience during this phase and suggests possible solutions.
Problem 1: Customers don't (really) know what they want
Possibly the most common problem in the requirements analysis phase is that
customers have only a vague idea of what they need and it's up to you to ask the right
questions and perform the analysis necessary to turn this amorphous vision into a
formally-documented software requirements specification that can, in turn, be used as
the basis for both a project plan and an engineering architecture.
To solve this problem, you should:
 Ensure that you spend sufficient time at the start of the project on
understanding the objectives, deliverables and scope of the project.
 Make visible any assumptions that the customer is using and critically evaluate
both the likely end-user benefits and risks of the project.
 Attempt to write a concrete vision statement for the project, which encompasses
both the specific functions or user benefits it provides and the overall business
problem it is expected to solve.
 Get your customer to read, think about and sign off on the completed software
requirements specification, to align expectations and ensure that both parties
have a clear understanding of the deliverable.
Problem 2: Requirements change during the course of the project
The second most common problem with software projects is that the
requirements defined in the first phase change as the project progresses. This may
occur because as development progresses and prototypes are developed, customers are
able to more clearly see problems with the original plan and make necessary course
corrections; it may also occur because changes in the external environment require
reshaping of the original business problem and hence necessitates a different solution
than the one originally proposed.
Good project managers are aware of these possibilities and typically already
have backup plans in place to deal with these changes.
 Have a clearly defined process for receiving, analyzing and incorporating change
requests, and make your customer aware of his/her entry point into this
process.
 Set milestones for each development phase beyond which certain changes are
not permissible -- for example, disallowing major changes once a module
reaches 75 percent completion.
 Ensure that change requests (and approvals) are clearly communicated to all
stakeholders, together with their rationale and that the master project plan is
updated accordingly.
Problem 3: Customers have unreasonable timelines
It's quite common to hear a customer say something like "it's an emergency job
and we need this project completed in X weeks". A common mistake is to agree to such
timelines before actually performing a detailed analysis and understanding both of the
scope of the project and the resources necessary to execute it. In accepting an
unreasonable timeline without discussion, you are, in fact, doing your customer a
disservice: it's quite likely that the project will either get delayed (because it wasn't
possible to execute it in time) or suffer from quality defects (because it was rushed
through without proper inspection).
 Convert the software requirements specification into a project plan, detailing
tasks and resources needed at each stage and modeling best-case, middle-case
and worst-case scenarios.
 Ensure that the project plan takes account of available resource constraints and
keeps sufficient time for testing and quality inspection.
 Enter into a conversation about deadlines with your customer, using the figures
in your draft plan as supporting evidence for your statements. Assuming that
your plan is reasonable, it's quite likely that the ensuing negotiation will be both
productive and result in a favorable outcome for both parties.
Problem 4: Communication gaps exist between customers, engineers and project
managers
Often, customers and engineers fail to communicate clearly with each other
because they come from different worlds and do not understand technical terms in the
same way. This can lead to confusion and severe miscommunication and an important
task of a project manager, especially during the requirements analysis phase, is to
ensure that both parties have a precise understanding of the deliverable and the tasks
needed to achieve it.
 Take notes at every meeting and disseminate these throughout the project team.
 Be consistent in your use of words. Make yourself a glossary of the terms that
you're going to use right at the start, ensure all stakeholders have a copy and
stick to them consistently.
Problem 5: The development team doesn't understand the politics of the
customer's organization
The scholars Bolman and Deal suggest that an effective manager is one who
views the organization as a "contested arena" and understands the importance of
power, conflict, negotiation and coalitions. Such a manager is not only skilled at
operational and functional tasks, but he or she also understands the importance of
framing agendas for common purposes, building coalitions that are united in their
perspective, and persuading resistant managers of the validity of a particular position.
These skills are critical when dealing with large projects in large organizations, as
information is often fragmented and requirements analysis is hence stymied by
problems of trust, internal conflicts of interest and information inefficiencies.
 Review your existing network and identify both the information you need and
who is likely to have it.
 Cultivate allies, build relationships and think systematically about your social
capital in the organization.
 Persuade opponents within your customer's organization by framing issues in a
way that is relevant to their own experience.
 Use initial points of access/leverage to move your agenda forward.
3.0 Spiral Model
History
The spiral model was defined by Barry Boehm in his 1988 article A Spiral
Model of Software Development and Enhancement. This model was not the first
model to discuss iterative development, but it was the first model to explain why the
iteration matters. As originally envisioned, the iterations were typically 6 months to 2
years long. Each phase starts with a design goal and ends with the client (who may be
internal) reviewing the progress thus far. Analysis and engineering efforts are applied
at each phase of the project, with an eye toward the end goal of the project.
The Spiral Model
The spiral model, also known as the spiral lifecycle model, is a systems
development method (SDM) used in information technology (IT). This model of
development combines the features of the prototyping model and the waterfall model.
The spiral model is intended for large, expensive and complicated projects. The steps
in the spiral model can be generalized as follows:
1. The new system requirements are defined in as much detail as possible. This
usually involves interviewing a number of users representing all the external or
internal users and other aspects of the existing system.
2. A preliminary design is created for the new system.
3. A first prototype of the new system is constructed from the preliminary design.
This is usually a scaled-down system and represents an approximation of the
characteristics of the final product.
4. A second prototype is evolved by a fourfold procedure: (1) evaluating the first
prototype in terms of its strengths, weaknesses, and risks; (2) defining the
requirements of the second prototype; (3) planning and designing the second
prototype; (4) constructing and testing the second prototype.
5. At the customer's option, the entire project can be aborted if the risk is deemed
too great. Risk factors might involve development cost overruns, operating-cost
miscalculation or any other factor that could, in the customer's judgment, result
in a less-than-satisfactory final product.
6. The existing prototype is evaluated in the same manner as was the previous
prototype, and if necessary, another prototype is developed from it according to
the fourfold procedure outlined above.
7. The preceding steps are iterated until the customer is satisfied that the refined
prototype represents the final product desired.
8. The final system is constructed, based on the refined prototype.
9. The final system is thoroughly evaluated and tested. Routine maintenance is
carried out on a continuing basis to prevent large-scale failures and to minimize
downtime.
Applications
For a typical shrink-wrap application, the spiral model might mean that you
have a rough-cut of user elements (without the polished / pretty graphics) as an
operable application, add features in phases, and, at some point, add the final
graphics. The spiral model is used most often in large projects. For smaller projects,
the concept of agile software development is becoming a viable alternative. The US
military has adopted the spiral model for its Future Combat Systems program.
Advantages of Spiral model
1. Estimates (i.e. budget, schedule, etc.) become more realistic as work progresses,
because important issues are discovered earlier.
2. It is more able to cope with the (nearly inevitable) changes that software
development generally entails.
3. Software engineers (who can get restless with protracted design processes) can
get their hands in and start working on a project earlier.
Disadvantages of Spiral model
1. Highly customized limiting re-usability
2. Applied differently for each application
3. Risk of not meeting budget or schedule
4.0 Prototype Model
A prototype is a working model that is functionally equivalent to a component of
the product. In many instances the client only has a general view of what is expected
from the software product. In such a scenario where there is an absence of detailed
information regarding the input to the system, the processing needs and the output
requirements, the prototyping model may be employed.
This model reflects an attempt to increase the flexibility of the development process by
allowing the client to interact and experiment with a working representation of the
product. The developmental process only continues once the client is satisfied with the
functioning of the prototype. At that stage the developer determines the specifications
of the client’s real needs.
The process of prototyping involves the following steps:
1. Identify basic requirements
Determine basic requirements including the input and output information
desired. Details, such as security, can typically be ignored.
2. Develop Initial Prototype
The initial prototype is developed that includes only user interfaces.
3. Review
The customers, including end-users, examine the prototype and provide
feedback on additions or changes.
4. Revise and Enhancing the Prototype
Using the feedback both the specifications and the prototype can be improved.
Negotiation about what is within the scope of the contract/product may be
necessary. If changes are introduced then a repeat of steps #3 ands #4 may be
needed.
Advantages of Prototyping
There are many advantages to using prototyping in software development, some
tangible some abstract.
Reduced time and costs: Prototyping can improve the quality of requirements and
specifications provided to developers. Because changes cost exponentially more to
implement as they are detected later in development, the early determination of what
the user really wants can result in faster and less expensive software.
Improved and increased user involvement: Prototyping requires user involvement
and allows them to see and interact with a prototype allowing them to provide better
and more complete feedback and specifications. The presence of the prototype being
examined by the user prevents many misunderstandings and miscommunications that
occur when each side believe the other understands what they said. Since users know
the problem domain better than anyone on the development team does, increased
interaction can result in final product that has greater tangible and intangible quality.
The final product is more likely to satisfy the users desire for look, feel and
performance.
Disadvantages of Prototyping
Using, or perhaps misusing, prototyping can also have disadvantages.
Insufficient analysis: The focus on a limited prototype can distract developers from
properly analyzing the complete project. This can lead to overlooking better solutions,
preparation of incomplete specifications or the conversion of limited prototypes into

poorly engineered final projects that are hard to maintain. Further, since a prototype is
limited in functionality it may not scale well if the prototype is used as the basis of a
final deliverable, which may not be noticed if developers are too focused on building a
prototype as a model.
User confusion of prototype and finished system: Users can begin to think that a
prototype, intended to be thrown away, is actually a final system that merely needs to
be finished or polished. (They are, for example, often unaware of the effort needed to
add error-checking and security features which a prototype may not have.) This can
lead them to expect the prototype to accurately model the performance of the final
system when this is not the intent of the developers. Users can also become attached to
features that were included in a prototype for consideration and then removed from the
specification for a final system. If users are able to require all proposed features be
included in the final system this can lead to feature creep.
Developer attachment to prototype: Developers can also become attached to
prototypes they have spent a great deal of effort producing; this can lead to problems
like attempting to convert a limited prototype into a final system when it does not have
an appropriate underlying architecture. (This may suggest that throwaway prototyping,
rather than evolutionary prototyping, should be used.)
Excessive development time of the prototype: A key property to prototyping is the
fact that it is supposed to be done quickly. If the developers lose sight of this fact, they
very well may try to develop a prototype that is too complex. When the prototype is
thrown away the precisely developed requirements that it provides may not yield a
sufficient increase in productivity to make up for the time spent developing the
prototype. Users can become stuck in debates over details of the prototype, holding up
the development team and delaying the final product.
Expense of implementing prototyping: the start up costs for building a development
team focused on prototyping may be high. Many companies have development
methodologies in place, and changing them can mean retraining, retooling or both.
Many companies tend to just jump into the prototyping without bothering to retrain
their workers as much as they should.
A common problem with adopting prototyping technology is high expectations
for productivity with insufficient effort behind the learning curve. In addition to
training for the use of a prototyping technique, there is an often overlooked need for
developing corporate and project specific underlying structure to support the
technology. When this underlying structure is omitted, lower productivity can often
result.
Best projects to use Prototyping

It has been argued that prototyping, in some form or another, should be used
all the time. However, prototyping is most beneficial in systems that will have many
interactions with the users.
It has been found that prototyping is very effective in the analysis and design of
on-line systems, especially for transaction processing, where the use of screen dialogs
is much more in evidence. The greater the interaction between the computer and the
user, the greater the benefit is that can be obtained from building a quick system and
letting the user play with it.
Systems with little user interaction, such as batch processing or systems that
mostly do calculations, benefit little from prototyping. Sometimes, the coding needed to
perform the system functions may be too intensive and the potential gains that
prototyping could provide are too small.
Prototyping is especially good for designing good human-computer interfaces.
"One of the most productive uses of rapid prototyping to date has been as a tool for
iterative user requirements engineering and human-computer interface design."
Some other process models:
Iterative Model
An iterative lifecycle model does not attempt to start with a full specification of
requirements. Instead, development begins by specifying and implementing just part of
the software, which can then be reviewed in order to identify further requirements.
This process is then repeated, producing a new version of the software for each cycle of
the model. Consider an iterative lifecycle model which consists of repeating the
following four phases in sequence:
A Requirements phase, in which the requirements for the software are gathered and
analyzed. Iteration should eventually result in a requirements phase that produces a
complete and final specification of requirements.
A Design phase, in which a software solution to meet the requirements is
designed. This may be a new design or an extension of an earlier design. An
Implementation and Test phase, when the software is coded, Integrated and tested.
A Review phase, in which the software is evaluated, the current requirements are
reviewed and changes and additions to requirements proposed. For each cycle of the
model, a decision has to be made as to whether the software produced by the cycle will
be discarded, or kept as a starting point for the next cycle (sometimes referred to as
incremental prototyping). Eventually a point will be reached where the requirements
are complete and the software can be delivered, or it becomes impossible to enhance
the software as required, and a fresh start has to be made. The iterative lifecycle model
can be likened to producing software by successive approximation. Drawing an analogy
with mathematical methods that use successive approximation to arrive at a final
solution, the benefit of such methods depends on how rapidly they converge on a
solution.
The key to successful use of an iterative software development lifecycle is
rigorous validation of requirements and verification (including testing) of each version
of the software against those requirements within each cycle of the model. The first
three phases of the example iterative model is in fact an abbreviated form of a
sequential V or waterfall lifecycle model. Each cycle of the model produces software
that requires testing at the unit level, for software integration, for system integration
and for acceptance. As the software evolves through successive cycles, tests have to be
repeated and extended to verify each version of the software.
RAD Model
Introduction
RAD is a linear sequential software development process model that emphasis
an extremely short development cycle using a component based construction
approach. If the requirements are well understood and defines and the project scope is
constraint, the RAD process enables a development team to create a fully functional
system with in very short time period.
What is RAD?
RAD (rapid application development) is a concept that products can be developed
faster and of higher quality through:
 Gathering requirements using workshops or focus groups
 Prototyping and early, reiterative user testing of designs
 The re-use of software components
 A rigidly paced schedule that defers design improvements to the next product
version
 Less formality in reviews and other team communication
Some companies offer products that provide some or all of the tools for RAD software
development. (The concept can be applied to hardware development as well.) These
products include requirements gathering tools, prototyping tools, computer-aided
software engineering tools, language development environments such as those for the
Java platform, groupware for communication among development members, and
testing tools. RAD usually embraces object-oriented programming methodology, which
inherently fosters software re-use. The most popular object-oriented programming
languages, C++ and Java are offered in visual programming packages often described
as providing rapid application development.
Development Methodology
The traditional software development cycle follows a rigid sequence of steps with
a formal sign-off at the completion of each. A complete, detailed requirements analysis
is done that attempts to capture the system requirements in a Requirements
Specification. Users are forced to "sign-off" on the specification before development
proceeds to the next step. This is followed by a complete system design and then
development and testing.
But, what if the design phase uncovers requirements that are technically
unfeasible, or extremely expensive to implement? What if errors in the design are
encountered during the build phase? The elapsed time between the initial analysis and
testing is usually a period of several months. What if business requirements or
priorities change or the users realize they overlooked critical needs during the analysis
phase? These are many of the reasons why software development projects either fail or
don’t meet the user’s expectations when delivered. RAD is a methodology for
compressing the analysis, design, build and test phases into a series of short, iterative
development cycles. This has a number of distinct advantages over the traditional
sequential development model.
RAD projects are typically staffed with small integrated teams comprised of
developers, end users and IT technical resources. Small teams, combined with short,
iterative development cycles optimizes speed, unity of vision and purpose, effective
informal communication and simple project management.
RAD Model Phases
RAD model has the following phases:
1. Business Modeling:
The information flow among business functions is defined by answering
questions like what information drives the business process, what information
is generated, who generates it, where does the information go, who process it
and so on.
2. Data Modeling:
The information collected from business modeling is refined into a set of data
objects (entities) that are needed to support the business. The attributes
(character of each entity) are identified and the relation between these data
objects (entities) is defined.
3. Process Modeling:
The data object defined in the data modeling phase are transformed to achieve
the information flow necessary to implement a business function. Processing
descriptions are created for adding, modifying, deleting or retrieving a data
object.
4. Application Generation:
Automated tools are used to facilitate construction of the software; even they
use the 4th GL techniques.
5. Testing and Turn over:
Many of the programming components have already been tested since RAD
emphasis reuse. This reduces overall testing time. But new components must
be tested and all interfaces must be fully exercised.
Advantages and Disadvantages
RAD reduces the development time and reusability of components help to speed
up development. All functions are modularized so it is easy to work with. For large
projects RAD require highly skilled engineers in the team. Both end customer and
developer should be committed to complete the system in a much abbreviated time
frame. If commitment is lacking RAD will fail. RAD is based on Object Oriented
approach and if it is difficult to modularize the project the RAD may not work well.
5.0 Tools and Techniques for process modeling
There are many choices for modeling tools and techniques, once you decide
what you want to capture in your process model. The appropriate technique for you
depends on your goals and your preferred work style. In particular, your choice for
notation depends on what you want to capture in your model. The notations range
from textual ones that express processes as functions to graphical ones that depict
processes as hierarchies of boxes and arrows to combination of pictures and text that
link the graphical depiction to tables and functions elaborating on the high level
illustration. Many of the modeling notations can also be used for representing
requirements and designs.
In this section the notation is secondary to the type of model and we focus on
two major categories, static and dynamic. A static model depicts the process showing
that the inputs are transformed to outputs. A dynamic model enacts the process so the
user can see how intermediate and final products are transformed over time.
Static modeling
There are many ways to model a process statistically. In the early 1990s, Lai
(1991) develop a comprehensive process notation that is intended to enable someone to
model any process at any level of detail. It builds on a paradigm where people perform
roles while resources perform activities leading to the production of artifacts. The
process model shows the relationship among the roles, activities and artifacts and
state tables show information about the completeness of each artifacts at a given time.
In particular, the elements of a process are viewed in terms of seven types:
1. Activity: Something that will happen in a process. The element can be related
to what happens before and after, what resources are needed, what triggers the
activity’s start, what rules govern the activity, how to describe the algorithms
and lessons learned and how to relate the activity to the project team.
2. Sequence: The order of activities. The sequence can be described using triggers,
programming constructs, transformations, ordering or satisfaction of
conditions.
3. Process model: A view of interest about the system. Thus part of the process
may be represented as a separate model, either to predict process behavior or to
examine certain characteristics.
4. Resources: A necessary item a tool or a person. Resources can include
equipment, time, office space, people and techniques and so on. The process
model identifies how much of each resource is needed for each activity.
5. Control: An external influence over process enactment. The controls may be
manual or automatic, human or mechanical.
6. Policy: A guiding principle. The high level process constraint influences process
enactment. It may include a prescribed development process, a tool that must
be used or a mandatory management style.
7. Organization: The hierarchical structure of process agents, with physical
grouping corresponding to logical grouping and related roles. The mapping from
physical to logical grouping should be flexible enough to reflect changes in

physical environment.
The process itself has several levels of abstraction, including the software
development process that directs certain resources to be used in constructing specific
modules as well as generic models that may resemble the spiral or waterfall models,
Lai’s notation includes several templates such as an Artifact Definition Template,
which records information about particular artifacts.
Lai’s approach can be applied to modeling software development processes; later in
this section, we use it to model the risk involved in development. However to
demonstrate its use and its ability to capture many facets of a complex activity, we
apply it to a relatively simple but familiar process, driving an automobile. Table
contains a description of the key resource in this process, a car.
Other templates define relations, process states, operations, analysis, actions
and roles. Graphical diagrams represent the relationship between elements capturing
the main relationships and secondary ones. For example figure given below illustrates
the process of starting a car. The “initiate” box represents the entrance conditions and
the “park” represents an exit condition. The left hand column of a condition box lists
artifacts and the right hand column is artifact state.
Transition diagram supplement the process model by showing how the states
are related to one another. For example, figure illustrates the transitions for a car.
Lai’s notation is a good example of how multiple structures and strategies can
be used to capture a great deal of information about the software development process
but it is also useful in organizing and depicting process information about user
requirements as the car example demonstrates.
6.0 Summary:
In this lesson we have studied the various process models which are used
during the development process of software, such as Waterfall model, Spiral model,
Prototyping model etc. We have also studied the tools and techniques for process
modeling
7.0 Self Check Exercise:
Q: Write detailed notes on the following:
 Waterfall model
 Spiral model
 Prototyping model
Hill Publications.
Publishers.
M.Sc (IT) Part-II PAPER: MS(A)-213
Project Planning and Organization
1. Management Functions
2. Project planning
2.1 Software productivity
2.2 People and productivity
2.3 Cost estimation
3. Project Control
3.1 Work breakdown structure
3.2 Gantt charts
3.3 PERT charts
4. Organization
4.1 Centralized control team organization
4.2 Decentralized control team organization
4.3 Mixed control team organization
4.4 An assessment of team organization
1. Management Functions
Management can be defined informally as the art of getting work done through
other people. But a more classic definition that allows a systematic study of the
subject is the following from Koontz:
The creation and maintenance of an internal environment in an
enterprise where individuals, working together in groups, can perform
efficiently and effectively toward the attainment of group goals.
Thus, the main job of management is to enable a group of people to work towards
a common goal. Management is not an exact science and the many activities involved
in it may be classified according to many different schemes. It is standard, however,
to consider management as consisting of the following five interrelated activities, the
goal of which is to achieve effective group work:
 Planning. A manager must decide what objectives are to be achieved, what
resources are required to achieve the objectives, how and when the resources
are to be acquired and how the goals are to be achieved. Planning basically
involves determining the flow of information, people and products within the
organization.
 Organizing. Organizing involves the establishment of clear lines of authority

and responsibility for groups of activities that achieve the goals of the
enterprise. Organizing is necessary at the level of a small group, such as a
five-person team of software engineers, all the way up to a large corporation
composed of several independent divisions. The choice of the organizational
structure affects the efficiency of the enterprise. Clearly, the best
organizational structure can be devised only when the goals of the enterprise
are clear, and this depends on effective planning.
 Staffing. Staffing deals with hiring personnel for the positions that are
identified by the organizational structure. It involves defining requirements for
personnel; recruiting (identifying, interviewing, and selecting candidates);
compensating, developing, and promoting employees.
 Directing. Directing involves leading subordinates. The goal of directing is to
guide the subordinates to understand and identify with the organizational
structure and the goals of the enterprise. This understanding must be
continuously refined by effective and exemplary leadership. Setting examples
is especially important in software engineering, where measures of good
engineering are lacking. The best training for a beginning engineer is to work
alongside a good engineer.
 Controlling. Controlling consists of measuring and correcting activities to
ensure that goals are achieved. Controlling requires the measurement of
performance against plans and taking corrective action when deviations occur.
These general functions of management apply to any management activity,
whether in a software engineering organization, an automobile manufacturing plant,
or a boy scout group.
2. Project planning
The first component of software engineering project management is effective
planning of the development of the software. The first step in planning a project is to
define and document the assumptions, goals and constraints of the project. A project
needs a clear statement of goals to guide the engineers in their daily decision making.
Many engineers on typical projects spend many hours discussing alternatives that
are known clearly to the project manager, but have not been documented or
disseminated properly. The goal of the project planning stage is to identify all the
external requirements for and constraints on the project.
Once the external constraints have been identified, the project manager must
come up with a plan for how best to meet the requirements within the given
constraints. One of the questions at this stage is what process model will serve the
project best. Another critical decision is to determine the resources required for the
project. The resources include the number and skill level of the people, the amount of
computing resources (e.g. workstations, personal computers, and data-base

software). Unlike traditional engineering disciplines, where one has to budget for
material, the "raw material" used in software is mainly the engineer's brain power.
Thus, the cost of a software project is directly proportional to the number of
engineers needed for the project. The problem of predicting how many engineers and
other resources are needed for a given software project is known as software cost
estimation.
Forecasting how many engineers will be needed is a difficult problem that is
intimately tied to the problem of how to estimate the productivity of software
engineers. There are two parts to the forecasting problem: estimating the difficulty of
the task and estimating how much of the task each engineer can solve. Clearly, to
estimate the difficulty of the task, one must know what the task is-that is, it is often
difficult to specify the software requirement completely.
Incomplete and imprecise requirements hinder accurate cost estimation. The
clearer and more complete the requirements are the easier it is to determine the
resources required. But even when the requirements are clear, estimating the
number of engineers needed is a formidable task with inherent difficulties. The best
approach is to develop the resource requirements incrementally, revising current
estimates as more information becomes available. We have seen that an appropriate
adaptation of the spiral model allows one to start with an estimate in the early
planning stages and revise the estimate for the remaining tasks at the conclusion of
each iteration of the particular phase of the life cycle.
How long it will take a software engineer to accomplish a given task is primarily a
function of the complexity of the problem, the engineer's ability, the design of the
software, and the tools that are available. For example, adding an undo facility to an
editor may require adding a new module or a complete redesign, depending on the
current design of the editor. Similarly, writing a front-end parser for a system may be
a simple task for an engineer who is familiar with parsing technology, but an
extremely difficult task for an engineer who is unaware of and thus tries to reinvent,
the parsing technology. Finally, writing a compiler with compiler development tools is
a lot easier than writing it without them.
We have already observed that management decisions have a strong impact on
the technical aspects of a project. We can see another example of this in the interplay
between management planning and the entire software life cycle. For example, the
choice of an evolutionary process model will impose a different kind of resource
planning from a waterfall model. While an evolutionary model allows the manager to
do resource planning incrementally as more information becomes available, it also
requires more flexibility from the manager. Similarly, an incremental model will affect
the resource loading for the different phases of the project, such as design and
testing, because the phases are iterated incrementally. In general, there is a strong
interplay between management functions and the technical aspects of a software
engineering project.
2.1 Software Productivity
One of the basic requirements of management in any engineering discipline is
to measure the productivity of the people and processes involved in production. The
measurements obtained are used during the planning phase of a project as the basis
for estimating resource requirements they also form the basis for evaluating the
performance of individuals, processes and tools, the benefits of automation, etc. The
ability to quantify the impact of various factors on productivity is important if we
want to evaluate the many claims made for various "productivity improvement" tools.
Indeed, there is an economic need for continued improvements in productivity. Of
course, we can be sure that we are making improvements only if we can measure
productivity quantitatively. Unfortunately, few, if any, claims made about tools that
improve software productivity are based on quantitative measurements.
2.1.1 Productivity metrics
We clearly need a metric for measuring software productivity. In
manufacturing-dominated disciplines such as automobile or television production,
there are simple and obvious metrics to use. For instance, a new manufacturing
process that doubles the number of television sets produced per day has doubled the
productivity of the factory. By taking into account the costs of the new process, we
can determine whether adopting the new process was cost effective.
The situation is not so simple in a design-dominated discipline like software
engineering. To begin with, there is the problem that software does not scale up. For
example, the typical productivity figure reported for professional software engineers is
a few tens of lines of code per day. If we compare this number with the productivity
exhibited on a student project, even beginning students appear to be much more
productive than professionals if we simply extrapolate from what they produce on a
simple homework project.
There are, however, several obvious reasons why a professional's productivity
appears to be less than a student's. First, students work longer hours, especially
when a project is due. Second, professionals don't spend the majority of their time in
strictly engineering activities: up to half their time may be taken up by meetings,
administrative matters, communication with team members, etc. One study showed
that up to 40% of a typical workweek is spent on nontechnical work. This implies
that the larger a programming team becomes, the lower the individual productivity
figures will be. Third, the professional has to meet more quality requirements than
the student: reliability, maintainability, performance, etc. Fourth, the inherent
complexity of the application affects the programmer's productivity. For example,
experience has shown that programmers developing application programs produce as

much as three times as many lines per day as programmers working on systems
programs.
An ideal productivity metric measure not lines of code but the amount of
functionality produced per unit time. The problem, however, is that we have no good
way of quantifying the concept of functionality. For example, how can we relate the
functionality offered by a payroll system to that offered by an air-traffic monitoring
system? We need to either find a measure that somehow takes into account the
inherent complexities of the different application areas or develop metrics that are
specialized to different application areas. One such metric, developed for information
systems, is called function points and is described in the next subsection.
Because of the difficulty of quantifying functionality, the search for
productivity metric has for the most part concentrated on the most tangible product
of software engineering: the actual code produced by the engineer. Various metrics
based on code size have been developed. These will be discussed below, following our
discussion of function points.
Function points
Function points attempt to quantify the functionality of a software system.
The goal is to arrive at a single number that completely characterizes the system and
correlates with observed programmer productivity figures. Actually, the function
point characterizes the complexity of the software system and thus can be used to
forecast how long it will take to develop the system and how many people will be
needed to do it.
The function point method was derived empirically, and the limited amount of
experimentation to date shows that it applies well in business applications, e.g.,
information systems. For example, using the function point as productivity metric, it
has been possible to quantify improvements in productivity due to such factors as
the use of structured programming and high-level languages. We can measure
programmer productivity in terms of the number of function points produced per unit
time.
According to the function point method, five items determine the complexity of
an application and the function point of given software is the weighted sum of these
five items. The weights for these items have been derived empirically and validated by
observation on many projects. The items and their weights are shown in Table 1.
The number of inputs and outputs count the distinct number of items that the
user Provides to the system and the system provides to user, respectively. In
counting the number of outputs, a group of items such as a screen or a file counts as
one item-that is the individual items in the group are not counted separately. The
number of inquiries is the number of distinct interactive queries made by the user
that require specific action by the system. Files are any group of related information
that is maintained by the application on behalf of the user or for organizing the
application. This item reveals the bias of the method towards business data
processing. Finally, the number of interfaces is the number of external interfaces to
other systems, e.g., the number of files to be read or written to disk or transmitted or
received from other systems. Therefore, if the application reads a file that is produced
by another application, the file is counted twice, once as input and once as an
interface. If a file is maintained by two different applications, it is counted as an
interface for each application. The focus of function point analysis is to measure the
complexity of an application based only on the function the application is supposed
to provide to users. Therefore, any temporary files produced by the application are
not counted in the measure; only those files that are visible or required by the user
are so counted.
Item Weight
Number of inputs 4
Number of outputs 5
Number of inquiries 4
Number of files 10
Number of interface 7
Table 1 Function point items and weights.
Once we have a metric, we can use it in many ways. For example, we can
compute the productivity of an engineer or a team per month by dividing the function
point for an existing software by the number of months it took to develop the
software. Or we can divide the amount of money spent to produce the software by the
function point to compute how much each function point costs. Or again, we can
measure the error rate per function point by dividing the number of errors found by
the function point. These function point-related numbers can be recorded and used
as bases for future planning or interproject comparisons. If the function point is an
accurate description of product complexity, the numbers should be similar across
different projects.
Code size
Since software engineers are supposed to produce software, the most tangible
product of software engineering is the running software that they deliver to their
clients. This has led many people to use the size of the software as a measure of
productivity. Of course the size of the software is not necessarily an indication of how
much effort went into producing it and a programmer that produces twice as many
statements as another is not necessarily twice as productive. In general, the size of
code is not a measure of any quality: a program that is twice as big as another is not
necessarily twice as good in any sense. Nevertheless, the most commonly used
metrics to measure productivity are based on code size, for example, in terms of
number of source lines produced per unit time
Of course, a code size metric has to be qualified immediately by considering
the same issues: Should we count comment lines? Should we count programming
language "statements" or simply the number of line feeds? How many times should
we count the lines in a file that is "included" several times? And should we count
declarations or just executable statements? By choosing answers to these questions,
we arrive at a particular productivity metric based on code size.
Two of the most common code size metrics are DSI (delivered source
instructions) in which only lines of code delivered to the customer are counted and
NCSS (non-commented source statements), in which comment lines are not counted.
We will use the generic unit, KLOC (thousands of lines of code), when we do not want
to distinguish between the specific metrics.
Lines of code have been used as productivity metric in many organizations.
The acceptance of this metric is due to the ease of measuring it and probably also
due to the fact that using any metric is better than using nothing at all. But at the
same time, we must be cognizant of the many problems associated with the measure.
At the heart of the problem is that there is no semantic content to code size; rather, it
is merely a function of the form of software. The following observations show some of
the deficiencies inherent in the metric.
Consider two programmers faced with the task of programming a module that
needs a sort operation internally. One programmer writes his own sort routine and
the other uses her time to find out how to use an existing sort routine in the system
library. Even though both accomplish the same task, the use of the library routine is
generally a better idea for many reasons: over the life of the software, the library
routine has less chances of containing errors and is better tested; it allows the
programmer to concentrate on the real problems she is trying to solve and perhaps
gain better insight into the application rather than code a sorting routine, etc. Yet,
the size metric penalizes the use of the library routine because it recognizes the other
programmer as more productive! In general, the size-based metric has the
unfortunate effect of equating software reuse with lower productivity.
When using lines of code as productivity metric, it is important to know what
lines are being counted in order to be able to make accurate comparisons between
individuals, projects or organizations. For example, an organization may produce
many software tools to support the development of a particular project. These tools
may become useful to many other projects in the organization, but are not delivered
to the customer as part of the product. Should these programs be counted in the
measurements? A commonly adopted convention is to count only the lines of code
that are delivered to the customer. To emphasize this decision, the models that use
the convention refer to the measure as KDSI-thousands of delivered source
instructions.
Using DSI as a measure does not mean that producing internal tools is a
worthless activity. In any engineering activity, investments in tools and a support
infrastructure are needed to improve productivity. The DSI measure focuses on the
objective of the engineering organization and separates the effort involved in building
internal tools from that involved in building the product itself. In other engineering
disciplines, too, productivity is measured in terms of the final product, not the
intermediate "mock-ups."
Another point to consider when comparing the productivity figures reported by
different organizations is whether the reported figures include the effect of cancelled
projects. For various reasons, many projects are cancelled in organizations before
they produce and deliver a product. Whether the lines of code of these cancelled
projects are counted in the overall productivity of the organization has a material
effect on the productivity figure. Such cancellations also have a secondary impact on
the motivational attitude of engineers in the organization and thus affect overall
productivity. Whatever criteria are used for measurement, comparison of figures is
possible only if the criteria are applied consistently. Since there are no standard
metrics, the use and interpretation of available productivity data in the literature
must be examined with-care.
Finally, lines of code are tied to line-oriented languages and are inherently
incapable of dealing with the emerging visual languages in which the programmer
uses diagrams or screen panels rather than statements.
2.1.2 Factors affecting productivity
Regardless of what metric we use for productivity, even if we simply have an
intuitive notion, there are factors that affect engineering productivity. One of the
important effects of productivity metric is that it allows the benefits of the various
factors to be quantified. By identifying the major contributors to productivity,
organizations can determine quantitatively whether they can afford to change their
practices, that is, whether the improvements in productivity are worth the expenses.
For example, will changing to a new programming language or adopting a new
development process, or hiring an efficiency expert or giving the engineers a private
office, or allowing them to work at home increase productivity sufficiently to justify
the expenses?
In one study that used lines of code as a metric, it was found that the single
most important factor affecting productivity was the capability of the personnel; half
as important, but second on the list, was the complexity of the product, followed by
required reliability and timing constraints (i.e., as in real-time software). The least
important items on the list were schedule constraints and language experience. The
effects of these various factors on productivity are reflected in the "cost driver
functions" used in cost estimation models, such as the COCOMO model which we
will see later.
Many managers believe that an aggressive schedule motivates the engineers to
do a better, faster job. However, experiments have shown that unrealistically
aggressive schedules not only cause the engineers to compromise on intangible
quality aspects, but also cause schedule delays. A surprising finding was that in a
controlled experiment, the subjects who had no schedule at all finished their project
first, beating out both the group that had an aggressive schedule and the one that
had a relaxed schedule. It has been shown that engineers, in general, are good at
achieving the one tangible goal that they have been given: if the primary goal is to
finish according to a given time schedule, they usually will-but at the cost of other
goals such as clarity of documentation and structure.
A specific example of the effect of overly aggressive scheduling can be seen
when design reviews are scheduled far too early in the development cycle. While the
manager may want to motivate the engineers to do the job in a shorter time, such
premature design reviews force the designer to document only issues that are well
understood and deny the reviewers an opportunity to provide useful feedback
There are many intangible factors that affect productivity and reduce the
credibility of the published numbers. Examples of these intangible factors are
personnel turnover, cancelled projects, reorganizations and restructuring of systems
for better quality.
2.2 People and Productivity
Because software engineering is predominantly an intellectual activity, the
most important ingredient for producing high-quality software efficiently is people. As
we mentioned in the last section, experimental evidence shows that the most
determinative factor of productivity is the capability of the engineers. Despite the
intuitive appeal of this notion and strong supporting empirical evidence, however,
many managers and organizations consistently behave as if they did not believe it.
Considering the main difference in software engineering competence between the best
and the worst engineers and the critical importance of engineering competence in
attaining high software productivity, the costs of hiring, retaining and motivating the
best engineers can be justified on economic ground.
Yet, a common misconception held by managers, as evidenced in their
staffing, planning, and scheduling practices, is that software engineers are
interchangeable that is that one software engineer is as productive as another. In fact
experiments have revealed a large variability in productivity between the best, the
average and the worst engineers. The worst engineers even reduce the productivity of
the team.
Apart from engineering capability, however, because of the strong amount of
interaction required among team members, the personalities of the members also
should be taken into account. Some engineers function better on centrally controlled
teams while others are better suited for teams with decentralized control. In short,
engineers are simply not interchangeable due to underlying reasons that have to do
with both technical ability and personality.
Another common management practice that fails to recognize the importance
of people in the software engineering process is that managers often staff a project
with the best available people rather than attempt to find the best people per se.
considering the strong impact of personnel capability on software costs; this is a
foolish thing to do. In effect, by assigning ill-prepared engineers to a project, the
manager is committing the organization to a training period for these engineers.
Since this commitment is unintentional and the training haphazard, there is a strong
risk that the training will be ineffective and the project unsuccessful or at least late.
The way to solve the Problem of finding appropriate people must be faced
deliberately: one must schedule the training period as a required task and train the
people appropriately, hire outside consultants and control the project closely,
perhaps by scheduling frequent incremental deliveries. The point is to recognize the
utter importance of key personnel to an intellectual project such as software
engineering.
2.3 Cost Estimation
Cost estimation is part of the planning stage of any engineering activity. The
difference in cost estimation between software engineering and other disciplines is
that in software engineering the primary cost is for people. In other engineering
disciplines the cost of materials-chips, bricks or aluminum, depending on the
activity-is a major component of the cost that must be estimated. In software
engineering, to estimate the cost we only have to estimate how many engineers are
needed.
Software cost estimation has two uses in software project management. First
during the planning stage, one needs to decide how many engineers are needed for
the project and develop a schedule. Second, in monitoring the project's progress, one
need to access whether the project is progressing according to schedule and take
corrective action if necessary. In monitoring progress, the manager needs to ascertain
how much work has already been accomplished and how much is left to do. Both of
these tasks require a metric for measuring "software work accomplishment."
Group Factor
Size Attributes Source Instruction

Object Instruction
Number of routines
Number of data items
Number of output formats
Documentation
Number of personnel
Programme Attributes Type

Complexity
Language
Reuse
Required reliability
Display requirements
Computer attributes Time Constraint

Storage constraint
Hardware configuration
Concurrent h/w development
Interfacing equipment, s/w
Personal attributes Personnel capability

Personnel continuity
Hardware experience
Application experience
Language experience
Project attributes Tools and techniques

Customer interface
Requirements definition
Requirements volatility
Schedule
Security
Computer access
Travel/rehosting/multi-site
Support software maturity
Table : 2 Factors used in cost estimation models

2.3.1 Predictive models of software cost

While lines of code are not an ideal metric of productivity, they do seem like an
appropriate metric for the total life cycle costs of software. That is the size of an
existing piece of software is a good measure of how hard it is to understand and
modify that software during its maintenance phase. Furthermore, if we could predict
how large a software system was going to be before developing it, that size could be
used as a basis for determining how much effort would be required, how many
engineers would be needed and how long it would take to develop the software. That
is, the size of the software can help us infer not just the initial development cost, but
also the costs associated with the later stages of the life cycle.
The majority of software cost estimation methods thus start by predicting the
size of the software and using that as input for deriving the total effort required, the
required effort during each phase of the life cycle, personnel requirements, etc. The
size estimate drives the entire estimation process. Inferring this initial estimate and
assessing its reliability can be considerably simpler if the organization maintains a
data base of information about past projects.
We can also base the initial estimate on an analytical technique such as
function point analysis. We first compute the function point for the desired software
and then divide it by the number FP/LOC for the project's programming language to
arrive-at a size estimate.
Besides basing the estimation of effort on code size, most cost estimation
models share certain other concepts. The purpose of a software cost model is to
predict the total development effort required to produce a given piece of software in
terms of the number of engineers and length of time it will take to develop the
software. The general formula used to arrive at the nominal development effort is
That is, the nominal number of programmer-months is derived on the basis of the
number of lines of code. The constants c and k are given by the model. The exponent
k is greater than 1, causing the estimated effort to grow nonlinearly with the code
size. To take into account the many variables that can affect the productivity of the
project, so-called cost drivers are used to scale this initial estimate of effort. For
example, if modern programming practices are being used, the estimate is scaled
downward; if there are realtime reliability requirements, the estimate is scaled
upward. The particular cost-driver items are determined and validated empirically. In
general, the cost drivers can be classified as being attributes of the following items:
 Product. For example, reliability requirements or inherent complexity.
 Computer. For example, are there execution time or storage constraints?
 Personnel. For example, are the personnel experienced in the application area
or the programming language being used?
 Project. For example, are sophisticated software tools being used?
Table 5: Efforts multipliers used by COCOMO intermediate model
The particular attributes in each class differ from model to model. For
example some models use object code for the size estimate, others use source code,
and some both object code and source code. Personnel attributes that can be
considered include the capability and continuity (lack of turnover) of the personnel.
Table 2 shows the set of factors considered by different models, classified into five
different groups of attributes. Size is separated into its own group because of its
importance and the different ways in which it is treated in the different models.
The basic steps for arriving at the cost of a proposed software system are the
following:
1. Estimate the software's eventual size, and use it in the model's formula to
arrive at an initial estimate of effort;
2. Revise the estimate by using the cost driver or other scaling factors given by
the model;
3. Apply the model's tools to the estimate derived in step 2 to determine the total
effort, activity distribution, etc.
The best known model for cost estimation today is the Constructive Cost
Model, known by its acronym, COCOMO. COCOMO is actually three different
models of increasing complexity and level of detail. We next give an overview of the
intermediate COCOMO model and the steps and details involved in the use of such a
model.
COCOMO
The following fleshes out how the general estimation steps described above apply
in the case of COCOMO:
1. The code size estimate is based on delivered source instructions, KDSI. The
initial (nominal) development effort is based on the project's development
"mode." COCOMO categorizes the software being developed according to
three modes; organic, semidetached, and embedded. Table 3 shows how to
determine which mode each project falls in. The estimator arrives at the
development mode by deciding which entries in the table best characterize the
project's features as listed in column 1. The heading on the column that best
matches the project is the development mode for the project. For example,
flight control software for a new fighter airplane falls into the embedded class,
and a standard payroll application falls into the organic class. Study the table
carefully to see the effect of the various features on the mode and, therefore, on
the development effort.
Each development mode has an associated formula for determining the nominal
development effort based on the estimated code size. The formulas are shown in
Table 4. Tables 3 and 4 together can be considered a quantitative summary of a
considerable amount of experimental data collected by Boehm over the years.
2. The estimator determines the effort multiplier for the particular project, based
on cost-driver attributes. COCOMO uses 15 cost-driver attributes to scale the
nominal development effort. These attributes are a subset of the general factors
listed in Table 2 and are given in Table 5, along with the multiplier used for
each, based on rating the driver for the particular project. There is a guideline for
determining how to rate each attribute for the project at hand. The rating ranges
from very low to extra high. The multipliers are multiplied together and with the
nominal effort derived in step 1 to arrive at the estimate of total effort for the
project.
Table 5 contains a wealth of information. For example, the range of the
multipliers for each factor shows the impact of that factor and how much control
the manager has over the factor. As an example, the range of analyst capability
shows that the difference between using an analyst of very high capability and
one of very low capability is a factor of two in the cost estimate. The product
attributes, in general, are fixed by the inherent complexity of the product and are
not within the control of the manager.
3. Given the estimate of the total effort in step 2, COCOMO allows the derivation of
various other important numbers and analyses. For example, Table 4 shows the
formulas, again based on the development mode, for deriving, a recommended
length for the project schedule based on the estimate of the total effort for the
project.
The COCOMO model allows sensitivity analyses based on changing the
parameters. For example, one can model the change in development time as a
function of relaxing the reliability constraints or improving the software development
environment. Or one can analyze the cost and impact of unstable hardware on a
project's software schedule.
Software cost estimation models such as COCOMO are required for an
engineering approach to software management. Without such models, one has only
judgment to rely on, which makes decisions hard to trust and justify. Worse, one can
never be sure whether improvements are being made to the software. While current
models still lack a full scientific justification, they can be used and validated against
an organization's project data base.
A software development organization should maintain a project data base
which stores information about the progress of projects. Such a data base can be
used in many ways: to validate a particular cost estimation model against past
projects; to calibrate cost-driver or scaling factors for a model, based on an
organization's particular environment; as a basis for arriving at an initial size
estimate; or to calibrate estimates of effort that are derived from a model.
With the current state of the art of cost estimation modelling, it is not wise
to have complete and blind trust in the results of the models, but a project manager
would be equally unwise to ignore the value of such tools for a software development
organization as a whole. They can be used to complement expert judgment and
intuition.
3. Project Control
As we said before, the purpose of controlling a project is to monitor the
progress of the activities against the plans, to ensure that the goals are being
approached and, eventually, achieved. Another aspect of control is to detect, as soon
as possible, when deviations from the plan are occurring so that corrective action
may be taken. In software engineering, as in any design-dominated discipline, it is
especially important to plan realistically-even conservatively-so as to minimize the
need for corrective action. For example, while in a manufacturing-dominated
discipline it may be justifiable to hire the minimum number of workers necessary
and add more employees if production falls below the required level, it has been
observed in software engineering that adding people to a project that is late can delay
the project even further. This underscores the importance of not only planning, but
also controlling, in software engineering. The sooner deviations from the plan are
detected, the more it is possible to cope with them.
3.1 Work Breakdown Structures
Most project control techniques are based on breaking down the goal of the
project into several intermediate goals. Each intermediate goal can in turn be broken
down further. This process can be repeated until each goal is small enough to be well
understood. We can then plan for each goal individually-its resource requirements,
assignment of responsibility, scheduling, etc.
A semiformal way of breaking down the goal is called the work breakdown
structure (WBS). In this technique, one builds a tree whose root is labelled by the
major activity of the project such as "build a compiler." Each node of the tree can be
broken down into smaller components that are designated the children of the node.
This "work breakdown" can be repeated until each leaf node in the tree is small
enough to allow the manager to estimate its size, difficulty and resource
requirements. Figure 1 shows the work breakdown structure for a simple compiler
development project.
The goal of a work breakdown structure is to identify all the activities that a
project must undertake. The tasks can be broken down into as fine a level of detail
as is desired or necessary. For example, we might have shown the substructure of a
node labelled "design" as consisting of the three different activities of designing the
scanner, parser, and code generator. The structure can be used as a basis for
estimating the amount of resources necessary for the project as a whole by
estimating the resources required for each leaf node.
The work breakdown structure is a simple tool that gives the manager a
framework for breaking down large tasks into more manageable pieces. Once these
manageable pieces have been identified, they can be used as units of work
assignment. The structure can be refined and extended easily by labeling the nodes
with appropriate information, such as the planned length of the activity, the name of
the person responsible for the activity and the starting and ending date of the
activity. In this way, the structure can summarize the project plans.
The work breakdown structure can also be an input into the scheduling
process, we will see in the following subsections. In breaking down the work, we are
trying decide which tasks need to be done. In scheduling, we decide the order in
which to do these tasks. Each work item in the work breakdown structure is
associated with an activity to perform that item. A schedule tries to order the
activities to ensure their timely completion.
Figure 1: Work breakdown structure for a complier project
Two general scheduling techniques are Gantt charts and PERT charts. We will
present these in the next two subsections.
3.2 Gantt Charts
Gantt charts (developed by Henry L. Gantt) are a project control technique
that can be used for several purposes, including scheduling, budgeting and resource
planning. A Gantt chart is a bar chart, with each bar representing an activity. The
bars are drawn against a time line. The length of each bar is proportional to the
length of time planned for the activity.
Let us draw a Gantt chart for the tasks identified in the WBS of Figure 1. We
estimate the number of days required for each of the six tasks as follows: initial
design, 45; scanner, 20; parser, 60; code generator, 180; integration and testing, 90;
and writing the manual, 90. Using these estimates, we can draw the Gantt chart of
Figure 2 for the compiler project.
A Gantt chart helps in scheduling the activities of a project, but it does not
help in identifying them. One can begin with the activities identified in the work
breakdown structure, as we did for the compiler example. During the scheduling
activity, and also during implementation of the project, new activities may be
identified that were not envisioned during the initial planning. The manager must
then go back and revise the Breakdown structure and the schedules to deal with
these new activities.
The Gantt chart in the figure is actually an enhanced version of standard

Gantt charts. The white part of the bar shows the length of time each task is
estimated to take. The gray part shows the "slack" time, that is, the latest time by
which a task must be finished. One way to view the slack time is that, if necessary,
we can slide the white area over the gray area without forcing the start of the next
activity to be delayed. For example, we have the freedom to delay the start of building
the scanner to as late as October 17, 1994 and still have it finished in time to avoid
delaying the integration and testing activity. The chart shows clearly that the results
of the scanner and parser tasks can be used only after the code generator task is
completed (in the integration and testing task). A bar that is all white, such as that
representing the code generator task, has no slack and must be started and
completed on the scheduled dates if the schedule is to be maintained. From the
figure, we can see that the three tasks, "design," "code generator," and "integration
and testing" have no slack. It is these tasks, then, that determine the total length of
time the project is expected to take.
This example shows that the Gantt chart can be used for resource allocation
and staff planning. For example, from Figure 2, we can conclude that the same
engineer can be assigned to do the scanner and the parser while another engineer is
working on the code generator. Even so, the first engineer will have some slack time
that we may plan to use to help the second engineer or to get a head start on the
integration and testing activity
Gantt charts can take different forms depending on their intended use. They are
best for resource scheduling. For example, if we are trying to schedule the activities of
six engineers, we might use a Gantt chart in which each bar represents one of the
engineers. In such a chart, the engineers are our resources and the chart shows the
resource loading during the project. It can help, for example, in scheduling vacation time,
or in ensuring that the right number of engineers will be available during each desired
period. Figure 3 shows an example. We could label appropriate sections of the bars to
show how much time we expect each engineer to spend on each activity (e.g., design
and building scanner).
Gantt charts are useful for resource planning and scheduling. While they
show the tasks and their durations clearly, however, they do not show intertask
dependencies plainly. PERT charts, the subject of the next section, show task
dependencies directly.
3.3 PERT Charts

A PERT (Program Evaluation and Review Technique) chart is a network of
boxes (or circles) and arrows. There are different variations of PERT charts. Some use
the boxes to represent activities and some use the arrows to do so. We will use the
first approach here. Each box thus represents an activity. The arrows are used to
show the dependencies of activities on one another. The activity at the head of an
arrow cannot start until the activity at the tail of the arrow is finished. Just as with
the nodes in the work breakdown structure, the boxes in a PERT chart can be
decorated with starting and ending dates for activities; the arrows help in computing
the earliest possible starting dates for the boxes at their heads. Some boxes can be
designated as milestones- A milestone is an activity whose completion signals an
important accomplishment in the life of the project. On the other hand, failure to
make a milestone signals trouble to the manager and requires an explicit action to
deal with the deviation from the schedule.
As with Gantt charts, to build a PERT chart for a project, one must first list all
the activities required for completion of the project and estimate how long each will
take, then one must determine the dependence of the activities on each other. The
PERT chart gives a graphical representation of this information. Clearly, the
technique does not help in deciding which activities are necessary or how long each
will take, but it does force the Manager to take the necessary planning steps to
answer these questions.
Figure 4 shows a PERT chart for the previous compiler project. The
information from the work breakdown structure of figure 1 is used to decide what
boxes we need. The arrows show the new information that was not available in the
work breakdown structure. The chart shows clearly that the project consists of the
activities of initial design, building a scanner, building a parser, building a code
generator, integrating and testing these and writing a manual. Recall that the
previous estimates for these six tasks were, respectively, 45, 20, 60, 180, 90, and 90
days.
The figure assumes that the project will start on January 1, 1994 (shown
underlined). Taking holidays into account (January 1 and 2 are holidays in 1994),
the design work will start on January 3, 1994. Since the design activity is estimated
to take 45 days, any activity that follows the design may start on March 7, 1994 at
the earliest. The dependency arrows help us compute these earliest start dates based
on our estimates of the duration of each activity. These dates are shown in the figure.
We could also compute the earliest finish dates or latest start dates or latest finish
dates, depending on the kind of analysis we want to perform.
The chart shows that the path through the project that consists of the
"design," "build code generator," and "integration and testing" activities is the critical
path for the project. Any delay in any activity in this path will cause a delay in the
entire project. The manager will clearly want to monitor the activities on the critical
path much more closely than the other activities.
Some of the advantages of PERT are as follows:

 It forces the manager to plan.
 It shows the interrelationships among the tasks in the project and in
particular. clearly identifies the critical path of the project, thus helping to
focus on it. For example, in the figure, the code generator is clearly the most
critical activity in terms of the schedule. The critical path is shown by a dark
solid line. We may decide to build a separate subproject for this activity alone,
or put our best people on the project or monitor the critical activity very
closely. The fact that the PERT chart has exposed the critical path allows us
the opportunity to consider alternative approaches to cope with a potential
problem.
 It exposes all possible parallelism in the activities and thus helps in allocating
resources.
 It allows scheduling and simulation of alternative schedules.
 It enables the manager to monitor and control the project.
4. Organization
The organizing function of management deals with devising roles for
individuals and assigning responsibility for accomplishing the project goals.
Organization is basically motivated by the need for cooperation when the goals are
not achievable by a single human being in a reasonable amount of time. The aim of
an organizational structure is to facilitate cooperation towards a common goal. An
organizational structure is necessary at any level of an enterprise, whether it is to
coordinate the efforts of a group of vice presidents who report to the president of the
corporation or to orchestrate the interactions among programmers who report to a

common project manager.
Management theorists and practitioners have studied the effects of different
organizational structures on the productivity and effectiveness of groups. Because
the goal of organization is to encourage cooperation and because cooperation
depends substantially on human characteristics, many general organizational rules
apply to any endeavor, whether it deals with software production or automobile
production.
The task of organizing can be viewed as building a team: given a group of
people and a common goal, what is the best role for each individual and how should
responsibilities be divided? Analogies with sports teams are illuminating. A
basketball team consists of five players on the floor who are playing the game and
another perhaps five players who are substitutes. Each player knows his role. There
is one ball, and at any one time, only one player can have it. All other players must
know their responsibilities and what to expect from the player with the ball. On well-
organized teams, the patterns of cooperation and their effects are clearly visible. In
poorly organized teams or teams with novice players, the lack of patterns of
cooperation is just as clearly visible: when one player has the ball, the other four
scream to be passed the ball. The player with the ball, of course, shoots the ball
instead of passing it to anyone!
Some of the considerations that affect the choice of an organization are similar
to the factors that are used in cost estimation models that we have seen earlier. For
example what constitutes an appropriate organization for a project depends on the
length of the project. Is it a long-term project or a short, one-shot project? If it is a
long-term project, it is important to ensure job satisfaction for individuals, leading to
high morale and thus reducing turnover. Sometimes, the best composition for a team
is a mix of junior and senior engineers. This allows the junior engineers to do the less
challenging tasks and learn from the senior engineers, who are busy doing the more
challenging tasks and overseeing the progress of the junior engineers.
Because of the nature of large software systems, changing requirements, and
the difficulties of software specification, it has been observed that adding people to a
project late in the development cycle leads to further delays in the schedule. Thus,
the issue of personnel turnover is a serious one that must be minimized. On a short-
term project, personnel turnover is not as important an issue. On a long-term
project, it is important to enable junior personnel to develop their skills and gain
more responsibility as senior personnel move on to other responsibilities. The trade-
offs involved in organizing for a short-term or a long-term project are similar to those
involved in organizing a basketball team to win a single game, to be a winner over a
single season or to be a consistent winner over many years.
Another issue affecting the appropriate project organization is the nature of

the task and how much communication the different team members need to have
among themselves. For example, in a well-defined task such as a payroll system in
which modules and their interfaces have been specified clearly, there is not much
need for project members to communicate among each other, and excessive
communication will probably lead to a delay in accomplishing their individual tasks.
On the other hand, in a project where the task is not clearly understood,
communication among team members is beneficial and can lead to a better solution.
Strictly hierarchical organizations minimize and discourage communication among
team members; more democratic organizations encourage it.
Software engineering requires not only the application of systematic
techniques to routine aspects of the software but also invention and ingenuity when
standard techniques are not adequate. Balancing these requirements is one of the
most difficult aspects of software engineering management
We can categorize software development team organizations according to
where decision-making control lies. A team can have centralized control, where a
recognized leader is responsible for and authorized to produce a design and resolve
all technical issues. Alternatively, a team organization can be based on distributing
decision-making control and emphasizing group consensus. Both of these types of
organization, as well as combinations of the two, have been used in practice
successfully. The following subsections discuss the two kinds of organization in more
detail.
4.1 Centralized control team organization
Centralized-control team organization is a standard management technique in
well understood disciplines. In this mode of organization, several workers report to a
supervisor who directly controls their tasks and is responsible for their performance.
Centralized control is based on a hierarchical organizational structure in which
several supervisors report to a "second-level" manager and so on up the chain to the
president of the enterprise. In general, centralized control works well with tasks that
are simple enough that the one person responsible for control of the project can
grasp the problem and its solution.
One way to centralize the control of a software development team is through a
chief programmer team. In this kind of organization, one engineer, known as the
chief programmer is responsible for the design and all the technical details of the
project. The chief programmer reports to a peer project manager who is responsible
for the administrative aspects of the project. Other members of the team are a
software librarian and other programmers who report to the chief programmer and
are added to the team on a temporary basis when needed. Specialists may be used by
the team as consultants when the need arises. The need for programmers and
consultants, as well as what tasks they perform, is determined by the chief

programmer, who initiates and controls all decisions. The software library maintained
by the librarian is the central repository for all the software, documentation, and
decisions made by the team. Figure 5 is a graphical representation of patterns of
control and communication supported by this kind of organization.
Chief programmer team organization has been likened to a surgical team
performing an operation. During an operation, one person must be clearly in control,
and all other people involved must be in total support of the "chief' surgeon; there is
no place or time for individual creativity or group consensus. This analogy highlights
the strengths of the chief programmer team organization, as well as its weaknesses.
The chief programmer team organization works well when the task is well
understood, is within the intellectual grasp of one individual and is such that the
importance of finishing the project outweighs other factors (such as team morale,
personnel development and life cycle costs).
On the negative side, a chief programmer team has a "single point of failure."
Since all communication must go through, and all decisions must be made by, the
chief programmer, the chief programmer may become overloaded or indeed,
saturated. The success of the chief programmer team clearly depends on the skill and
ability of the chief programmer and the size and complexity of the problem. The
choice of chief programmer is the most important determinant of success of the chief
programmer team. On the other hand, since there is a great variability in people's
abilities-as much as a 10-to-1 ratio in productivity-a chief programmer position may
be the best way to use the rare highly productive engineers.
4.2 Decentralized control team organization
In a decentralized-control team organization, decisions are made by consensus
and all work is considered group work. Team members review each other's work and
are responsible as a group for what every member produces. Figure 6 shows the
patterns of control and communication among team members in a decentralized-
control organization. The ring like management structure is intended to show the
lack of a hierarchy and that all team members are at the same level.
Such a "democratic" organization leads to higher morale and job satisfaction
and, therefore, to less turnover. The engineers feel more ownership of the project and
responsibility for the problem, leading to higher quality in their work. A decentralized
control organization is more suited for long-term projects because the amount of
intragroup communication that it encourages leads to a longer development time,
presumably accompanied by lower life cycle costs. The proponents of this kind of
team organization claim that it is more appropriate for less understood and more
complicated problems because a group can invent better solutions than a single
individual. Such an organization is based on a technique referred to as "ego less
programming" because it encourages programmers to share and review one another's

work.
On the negative side, decentralized-control team organization is not appropriate
for large teams, where the communication overhead can overwhelm all the engineers,
reducing individual productivity.
4.3 Mixed control team organization

A mixed control team organization attempts to combine the benefits of
centralized and decentralized control, while minimizing or avoiding their
disadvantages. Rather than treating all members the same as in a decentralized
organization or treating a single individual as the chief as in a decentralized
organization the mixed organization distinguishes the engineers into senior and
junior engineers. Each senior engineer leads a group of junior engineers. The senior
engineers in turn report to a project manager.
Control is vested in the project manager and senior programmers, while
communication is decentralized among each set of individuals, peers and their
immediate supervisors. The patterns of control and communication in mixed-control
organizations are shown in Figure 7.
A mixed-mode organization tries to limit communication to within a small

group that is most likely to benefit from it. It also tries to realize the benefits of group
decision making by vesting authority in a group of senior programmers. The mixed-
control organization is an example of the use of a hierarchy to master the complexity
of software development as well as organizational structure.
4.4 An assessment of team organization
In the previous subsections, we have presented different ways of organizing
software development teams. Each kind of organization discussed in the previous
three subsections has its proponents and detractors. Each also has its appropriate
place. Experimental assessment of different organizational structures is difficult. It is
clearly impractical to run large software development projects using two different
types of organization, just for the purpose of comparing the effectiveness of the two
structures. While cost estimation models can be assessed on the basis of how well
they predict actual software costs, an organizational structure cannot be assessed so
easily, because one cannot compare the results achieved with those one would have
achieved with a different organization. Experiments have been run to measure the
effects of such things as team size and task complexity on the effectiveness of
development teams. In the choice of team organization, however, it appears that we
must be content with the following general considerations:
 Just as no life cycle model is appropriate for all projects, no team organization
is appropriate for all tasks.
 Decentralized control is best when communication among engineers is
necessary for achieving a good solution.
 Centralized control is best when speed of development is the most important
goal and the problem is well understood.
 An appropriate organization tries to limit the amount of communication to
what is necessary for achieving project goals-no more and no less.
 An appropriate organization may have to take into account goals other than
speed of development. Among these other important goals are: lower life cycle
costs, reduced personnel turnover, repeatability of the process, development of
junior engineers into senior engineers and widespread dissemination of
specialized knowledge and expertise among personnel.
M.Sc. (IT) Part-II PAPER : MS(A)-213
The Requirement Analysis
1. Introduction
2. What is Requirements Analysis?
3. Steps in the Requirements Analysis Process
4. Types of Requirements
5. What is a Software Requirements Specification?
6. What are the benefits of a Great SRS?
7. What should the SRS address?
8. What are the characteristics of a great SRS?
9. What Kind of Information Should an SRS Include?
10. Conclusion
11. Suggested readings
1. Introduction
Before starting to design a software product, it is very important to understand
the precise requirements of the customer and to develop them properly. In the past
many projects have been suffered because the developers started implementing
something without determining whether they were building what the customer wanted.
Starting properly documented activities without improperly documented requirements
is the biggest mistake that one can commit during the product development.
Improperly documented requirements increase the number of iterative changes
required during the life cycle phases and thereby push up the development cost
tremendously. They also set the ground for bitter customer development disputes
protracted legal battles. Therefore requirement analysis and specification is considered
to be a very important phase of software development and has to be undertaken with utmost
care. It is the process of determining user expectations for a new or modified product.
2. What is Requirements Analysis?
Requirements Analysis is the process of understanding the customer needs
and expectations from a proposed system or application and is a well-defined
stage in the Software Development Life Cycle model. Requirements are a
description of how a system should behave or a description of system properties or
attributes. It can alternatively be a statement of ‘what’ an application is expected to do.
Given the multiple levels of interaction between users, business processes and devices
in global corporations today, there are simultaneous and complex requirements from a
single application, from various levels within an organization and outside it as well.
The Software Requirements Analysis Process covers the complex task of eliciting
and documenting the requirements of all these users, modeling and analyzing these
requirements and documenting them as a basis for system design. A dedicated and
specialized Requirements Analyst is best equipped to handle the job. The Requirements
Analysis function may also fall under the scope of Project Manager, Program Manager
or Business Analyst, depending on the organizational hierarchy. Software
Requirements Analysis and Documentation Processes are critical to software project
success. Requirements engineering is an emerging field which deals with the
systematic handling of requirements.
Why is Requirements Analysis necessary?
Studies reveal that inadequate attention to Software Requirements Analysis at
the beginning of a project is the most common cause for critically vulnerable projects
that often do not deliver even on the basic tasks for which they were designed. There
are instances of corporations that have spent huge amounts on software projects
where the end application eventually does not perform the tasks it was intended for.
Software companies are now investing time and resources into effective and
streamlined Software Requirements Analysis Processes as a prerequisite to successful
projects that align with the client’s business goals and meet the project’s requirement
specifications.
3. Steps in the Requirements Analysis Process
I. Fix system boundaries
This initial step helps in identifying how the new application integrates with the
business processes, how it fits into the larger picture and what its scope and
limitations will be.
II. Identify the customer
In more recent times there has been a focus on identifying who the ‘users’ or
‘customers’ of an application are. Referred to broadly as the ‘stake holders’, these
indicate the group or groups of people who will be directly or indirectly impacted by the
new application.
By defining in concrete terms who the intended user is, the Requirements
Analyst knows in advance where he has to look for answers. The Requirements
Elicitation Process should focus on the wish-list of this defined group to arrive at a
valid requirements list.
III. Requirements elicitation
Information is gathered from the multiple stakeholders identified. The
Requirements Analyst draws out from each of these groups what their requirements
from the application are and what they expect the application to accomplish.
Considering the multiple stakeholders involved, the list of requirements gathered in

this manner could run into pages. The level of detail of the requirements list is based
on the number and size of user groups, the degree of complexity of business processes
and the size of the application.
a) Problems faced in Requirements Elicitation
 Ambiguous understanding of processes
 Inconsistency within a single process by multiple users
 Insufficient input from stakeholders
 Conflicting stakeholder interests
 Changes in requirements after project has begun
A Requirements Analyst has to interact closely with multiple work-groups, often
with conflicting goals, to arrive at a bona-fide requirements list. Strong communication
and people skills along with sound programming knowledge are prerequisites for an
expert Requirements Analyst.
b) Tools used in Requirements Elicitation
Traditional methods of Requirements Elicitation included stakeholder interviews
and focus group studies. Other methods like flowcharting of business processes and
the use of existing documentation like user manuals, organizational charts, process
models and systems or process specifications, on-site analysis, interviews with end-
users, market research and competitor analysis were also used extensively in
Requirements Elicitation.
However current research in Software Requirements Analysis Process has
thrown up modern tools that are better equipped to handle the complex and
multilayered process of Requirements Elicitation. Some of the current Requirements
Elicitation tools in use are:
 Prototypes
 Use cases
 Data flow diagrams
 Transition process diagrams
 User interfaces
IV. Requirements Analysis Process
Once all stakeholder requirements have been gathered, a structured analysis of
these can be done after modeling the requirements. Some of the Software
Requirements Analysis techniques used are requirements animation, automated
reasoning, knowledge-based critiquing, consistency checking, analogical and case-
based reasoning.
V. Requirements Specification
Requirements, once elicited, modeled and analyzed should be documented in
clear, unambiguous terms. A written requirements document is critical so that its
circulation is possible among all stakeholders including the client, user-groups, the
development and testing teams. Current requirements engineering practices reveal that
a well-designed, clearly documented Requirements Specification is vital and serves as :
 Base for validating the stated requirements and resolving stakeholder conflicts,
if any
 Contract between the client and development team
 Basis for systems design for the development team
 Bench-mark for project managers for planning project development lifecycle and
goals
 Source for formulating test plans for QA and testing teams
 Resource for requirements management and requirements tracing
 Basis for evolving requirements over the project life span
Software requirements specification involves scoping the requirements so that it
meets the customer’s vision. It is the result of collaboration between the end-user who
is often not a technical expert and a Technical/Systems Analyst, who is likely to
approach the situation in technical terms.
The software requirements specification is a document that lists out
stakeholders’ needs and communicates these to the technical community that will
design and build the system. The challenge of a well-written requirements specification
is to clearly communicate to both these groups and all the sub-groups within.
To overcome this, Requirements Specifications may be documented separately as
 User Requirements - written in clear, precise language with plain text and use
cases, for the benefit of the customer and end-user
 System Requirements - expressed as a programming or mathematical model,
addressing the Application Development Team and QA and Testing Team.
Requirements Specification serves as a starting point for software, hardware
and database design. It describes the function (Functional and Non-Functional
specifications) of the system, performance of the system and the operational and user-
interface constraints that will govern system development.
VI. Requirements Management
Requirements Management is the comprehensive process that includes all
aspects of software requirements analysis and additionally ensures verification,
validation and traceability of requirements. Effective requirements management
practices guarantee that all system requirements are stated unambiguously, that
omissions and errors are corrected and that evolving specifications can be incorporated
later in the project lifecycle.
4. Types of Requirements
Requirements are categorized in several ways. The following are common
categorizations of requirements that relate to technical management:
Customer Requirements
Statements of fact and assumptions that define the expectations of the system
in terms of mission objectives, environment, constraints and measures of effectiveness
and suitability (MOE/MOS). The customers are those that perform the eight primary
functions of systems engineering, with special emphasis on the operator as the key
customer. Operational requirements will define the basic need and, at a minimum,
answer the questions posed in the following listing:
 Operational distribution or deployment: Where will the system be used?
 Mission profile or scenario: How will the system accomplish its mission
objective?
 Performance and related parameters: What are the critical system
parameters to accomplish the mission?
 Utilization environments: How are the various system components to be
used?
 Effectiveness requirements: How effective or efficient must the system be
in performing its mission?
 Operational life cycle: How long will the system be in use by the user?
 Environment: What environments will the system be expected to operate
in an effective manner?
Functional Requirements
Functional requirements explain what has to be done by identifying the
necessary task, action or activity that must be accomplished. Functional requirements
analysis will be used as the top level functions for functional analysis.
Performance Requirements
The extent to which a mission or function must be executed; generally
measured in terms of quantity, quality, coverage, timeliness or readiness. During
requirements analysis, performance (how well does it have to be done) requirements
will be interactively developed across all identified functions based on system life cycle
factors; and characterized in terms of the degree of certainty in their estimate, the
degree of criticality to system success and their relationship to other requirements.
Design Requirements
The “build to,” “code to,” and “buy to” requirements for products and “how to
execute” requirements for processes expressed in technical data packages and
technical manuals.
Derived Requirements
Requirements that are implied or transformed from higher-level requirement.
For example, a requirement for long range or high speed may result in a design
requirement for low weight.
Allocated Requirements
A requirement that is established by dividing or otherwise allocating a high-level
requirement into multiple lower-level requirements. Example: A 100-pound item that
consists of two subsystems might result in weight requirements of 70 pounds and 30
pounds for the two lower-level items.
5. What is a Software Requirements Specification?
An SRS is basically an organization's understanding (in writing) of a customer
or potential client's system requirements and dependencies at a particular point in
time (usually) prior to any actual design or development work. It's a two-way insurance
policy that assures that both the client and the organization understand the other's
requirements from that perspective at a given point in time.
The SRS document itself states in precise and explicit language those functions
and capabilities a software system (i.e., a software application, an eCommerce Web
site, and so on) must provide, as well as states any required constraints by which the
system must abide. The SRS also functions as a blueprint for completing a project with
as little cost growth as possible. The SRS is often referred to as the "parent" document
because all subsequent project management documents, such as design specifications,
statements of work, software architecture specifications, testing and validation plans
and documentation plans are related to it.
It's important to note that an SRS contains functional and nonfunctional
requirements only; it doesn't offer design suggestions, possible solutions to technology
or business issues or any other information other than what the development team
understands the customer's system requirements to be.
A well-designed, well-written SRS accomplishes four major goals:
 It provides feedback to the customer. An SRS is the customer's assurance
that the development organization understands the issues or problems to be
solved and the software behavior necessary to address those problems.
Therefore, the SRS should be written in natural language in an unambiguous
manner that may also include charts, tables, data flow diagrams, decision
tables and so on.
 It decomposes the problem into component parts. The simple act of writing
down software requirements in a well-designed format organizes information,
places borders around the problem, solidifies ideas and helps break down the
problem into its component parts in an orderly fashion.
 It serves as an input to the design specification. As mentioned previously,
the SRS serves as the parent document to subsequent documents, such as the
software design specification and statement of work. Therefore, the SRS must
contain sufficient detail in the functional system requirements so that a design
solution can be devised.
 It serves as a product validation check. The SRS also serves as the parent
document for testing and validation strategies that will be applied to the
requirements for verification.
SRSs are typically developed during the first stages of "Requirements
Development," which is the initial product development phase in which information is
gathered about what requirements are needed--and not. This information-gathering
stage can include onsite visits, questionnaires, surveys, interviews and perhaps a
return-on-investment (ROI) analysis or needs analysis of the customer or client's
current business environment. The actual specification then is written after the
requirements have been gathered and analyzed.
6. What are the benefits of a Great SRS?
The IEEE 830 standard defines the benefits of a good SRS:
Establish the basis for agreement between the customers and the suppliers on
what the software product is to do. The complete description of the functions to be
performed by the software specified in the SRS will assist the potential users to
determine if the software specified meets their needs or how the software must be
modified to meet their needs.
Reduce the development effort. The preparation of the SRS forces the various
concerned groups in the customer’s organization to consider rigorously all of the
requirements before design begins and reduces later redesign, recoding and retesting.
Careful review of the requirements in the SRS can reveal omissions,
misunderstandings, and inconsistencies early in the development cycle when these
problems are easier to correct.
Provide a basis for estimating costs and schedules. The description of the product
to be developed as given in the SRS is a realistic basis for estimating project costs and
can be used to obtain approval for bids or price estimates.
Provide a baseline for validation and verification. Organizations can develop their
validation and Verification plans much more productively from a good SRS. As a part of
the development contract, the SRS provides a baseline against which compliance can
be measured.
Facilitate transfer. The SRS makes it easier to transfer the software product to new
users or new machines. Customers thus find it easier to transfer the software to other
parts of their organization and suppliers find it easier to transfer it to new customers.
Serve as a basis for enhancement. Because the SRS discusses the product but not
the project that developed it, the SRS serves as a basis for later enhancement of the
finished product. The SRS may need to be altered, but it does provide a foundation for
continued production evaluation.
7. What should the SRS address?

Again from the IEEE standard:
The basic issues that the SRS writer(s) shall address are the following:
a) Functionality. What is the software supposed to do?
b) External interfaces. How does the software interact with people, the system’s
hardware, other hardware and other software?
c) Performance. What is the speed, availability, response time, recovery time of
various software functions, etc.?
d) Attributes. What is the portability, correctness, maintainability, security, etc.
considerations?
e) Design constraints imposed on an implementation. Are there any required
standards in effect, implementation language, policies for database integrity,
resource limits, operating environment(s) etc.?
8. What are the characteristics of a great SRS?
Again from the IEEE standard:
An SRS should be
 Correct
 Unambiguous
 Complete
 Consistent
 Ranked for importance and/or stability
 Verifiable
 Modifiable
 Traceable
Correct - This is like motherhood and apple pie. Of course you want the specification
to be correct. No one writes a specification that they know is incorrect. We like to say -
"Correct and Ever Correcting." The discipline is keeping the specification up to date
when you find things that are not correct.
Unambiguous - An SRS is unambiguous if, and only if, every requirement stated there
in has only one interpretation. Again, easier said than done. Spending time on this
area prior to releasing the SRS can be a waste of time. But as you find ambiguities - fix
them.
Complete - A simple judge of this is that is should be all that is needed by the software
designers to create the software.
Consistent - The SRS should be consistent within itself and consistent to its reference
documents. If you call an input "Start and Stop" in one place, don't call it "Start/Stop"
in another.
Ranked for Importance - Very often a new system has requirements that are really
marketing wish lists. Some may not be achievable. It is useful provide this information
in the SRS.
Verifiable - Don't put in requirements like - "It should provide the user a fast
response." Another of my favorites is - "The system should never crash." Instead,
provide a quantitative requirement like: "Every key stroke should provide a user
response within 100 milliseconds."
Modifiable - Having the same requirement in more than one place may not be wrong -
but tends to make the document not maintainable.
Traceable - Often, this is not important in a non-politicized environment. However, in
most organizations, it is sometimes useful to connect the requirements in the SRS to a
higher level document. Why do we need this requirement?
9. What Kind of Information Should an SRS Include?
You probably will be a member of the SRS team (if not, ask to be), which means
SRS development will be a collaborative effort for a particular project. In these cases,
your company will have developed SRSs before, so you should have examples (and,
likely, the company's SRS template) to use. But, let's assume you'll be starting from
scratch. Several standards organizations (including the IEEE) have identified nine
topics that must be addressed when designing and writing an SRS:
1. Interfaces
2. Functional Capabilities
3. Performance Levels
4. Data Structures/Elements
5. Safety
6. Reliability
7. Security/Privacy
8. Quality
9. Constraints and Limitations
But, how do these general topics translate into an SRS document? What,
specifically, does an SRS document include? How is it structured? And how do you get
started? An SRS document typically includes four ingredients, as discussed in the
following sections:
1. A template
2. A method for identifying requirements and linking sources
3. Business operation rules
4. A traceability matrix
Begin with an SRS Template
The first and biggest step to writing an SRS is to select an existing template that
you can fine tune for your organizational needs (if you don't have one already). There's
not a "standard specification template" for all projects in all industries because the
individual requirements that populate an SRS are unique not only from company to
company, but also from project to project within any one company. The key is to select
an existing template or specification to begin with and then adapt it to meet your
needs.
In recommending using existing templates, I'm not advocating simply copying a
template from available resources and using them as your own; instead, I'm suggesting
that you use available templates as guides for developing your own. It would be almost
impossible to find a specification or specification template that meets your particular
project requirements exactly. But using other templates as guides is how it's
recommended in the literature on specification development.
Look at what someone else has done, and modify it to fit your project
requirements.
Table 1 shows what a basic SRS outline might look like. This example is an adaptation
and extension of the IEEE Standard 830-1998:
Table 1 A sample of a basic SRS outline
1. Introduction
1.1 Purpose
1.2 Document conventions
1.3 Intended audience
1.4 Additional information
1.5 Contact information/SRS team members
1.6 References
2. Overall Description
2.1 Product perspective
2.2 Product functions
2.3 User classes and characteristics
2.4 Operating environment
2.5 User environment
2.6 Design/implementation constraints
2.7 Assumptions and dependencies
3. External Interface Requirements
3.1 User interfaces
3.2 Hardware interfaces
3.3 Software interfaces
3.4 Communication protocols and interfaces
4. System Features
4.1 System feature A
4.1.1 Description and priority

4.1.2 Action/result
4.1.3 Functional requirements
4.2 System feature B
5. Other Nonfunctional Requirements
5.1 Performance requirements
5.2 Safety requirements
5.3 Security requirements
5.4 Software quality attributes
5.5 Project documentation
5.6 User documentation
6. Other Requirements
Appendix A: Terminology/Glossary/Definitions list
Appendix B: To be determined
Table 2 shows a more detailed SRS outline

Table 2 A sample of a more detailed SRS outline
1. Scope 1.1 Identification.

Identify the system and the software to which this
document applies, including, as applicable, identification
number(s), title(s), abbreviation(s), version number(s), and
release
number(s).
1.2 System overview.
State the purpose of the system or subsystem to which this
document applies.
1.3 Document overview.
Summarize the purpose and contents of this document.
This document comprises six sections:
 Scope
 Referenced documents
 Requirements
 Qualification provisions
 Requirements traceability
 Notes
Describe any security or privacy considerations associated
with its use.
2. Referenced 2.1 Project documents.

Documents Identify the project management system documents here.
2.2 Other documents.
2.3 Precedence.
2.4 Source of documents.
3. Requirements This section shall be divided into paragraphs to specify the

Computer Software Configuration Item (CSCI)
requirements, that is, those characteristics of the CSCI
that are conditions for its acceptance. CSCI requirements
are software requirements generated to satisfy the system
requirements allocated to this CSCI. Each requirement
shall be assigned a project-unique identifier to support
testing and traceability and shall be stated in such a way
that an objective test can be defined for it.
3.1 Required states and modes.
3.2 CSCI capability requirements.
3.3 CSCI external interface requirements.
3.4 CSCI internal interface requirements.
3.5 CSCI internal data requirements.
3.6 Adaptation requirements.
3.7 Safety requirements.
3.8 Security and privacy requirements.
3.9 CSCI environment requirements.
3.10 Computer resource requirements.
3.11 Software quality factors.
3.12 Design and implementation constraints.
3.13 Personnel requirements.
3.14 Training-related requirements.
3.15 Logistics-related requirements.
3.16 Other requirements.
3.17 Packaging requirements.
3.18 Precedence and criticality requirements.
4. Qualification To be determined.
Provisions
5. Requirements To be determined.
Traceability
6. Notes This section contains information of a general or

explanatory nature that may be helpful, but is not
mandatory.
6.1 Intended use.
This Software Requirements specification shall...
6.2 Definitions used in this document.
Insert here an alphabetic list of definitions and their source
if different from the declared sources specified in the
"Documentation standard."
6.3 Abbreviations used in this document.
Insert here an alphabetic list of the abbreviations and
acronyms if not identified in the declared sources specified
in the "Documentation Standard."
6.4 Changes from previous issue.
Will be "not applicable" for the initial issue.
Revisions shall identify the method used to identify
changes from the previous issue.
Identify and Link Requirements with Sources

As noted earlier, the SRS serves to define the functional and nonfunctional
requirements of the product. Functional requirements each have an origin from which
they came, be it a use case (which is used in system analysis to identify, clarify and
organize system requirements and consists of a set of possible sequences of
interactions between systems and users in a particular environment and related to a
particular goal), government regulation, industry standard or a business requirement.
In developing an SRS, you need to identify these origins and link them to their
corresponding requirements. Such a practice not only justifies the requirement, but it
also helps assure project stakeholders that frivolous or spurious requirements are kept
out of the specification.
To link requirements with their sources, each requirement included in the SRS
should be labeled with a unique identifier that can remain valid over time as
requirements are added, deleted or changed. Such a labeling system helps maintain
change-record integrity while also serving as an identification system for gathering
metrics. You can begin a separate requirements identification list that ties a
requirement identification (ID) number with a description of the requirement.
Eventually, that requirement ID and description become part of the SRS itself and then
part of the Requirements Traceability Matrix, discussed in subsequent paragraphs.
Table 3 illustrates how these SRS ingredients work together.
Table 3 This sample table identifies requirements and links them to their sources
ID Paragraph Requirement Business Rule Source

No. No.
17 5.1.4.1 Understand/communicate IEEE STD xx-xxxx

using SMTP protocol

using POP protocol

using IMAP protocol
20 5.1.4.2 Open at same rate as OE Use Case Doc 4.5.4
Establish Business Rules for Contingencies and Responsibilities
"The best-laid plans of mice and men..." begins the famous saying. It has direct
application to writing SRSs because even the most thought-out requirements are not
immune to changes in industry, market or government regulations. A top-quality SRS
should include plans for planned and unplanned contingencies, as well as an explicit
definition of the responsibilities of each party, should a contingency be implemented.
Some business rules are easier to work around than others, when Plan B has to be
invoked. For example, if a customer wants to change a requirement that is tied to a
government regulation, it may not be ethical and/or legal to be following "the spirit of
the law." Many government regulations, as business rules, simply don't allow any
compromise or "wiggle room." A project manager may be responsible for ensuring that
a government regulation is followed as it relates to a project requirement; however, if a
contingency is required, then the responsibility for that requirement may shift from the
project manager to a regulatory attorney. The SRS should anticipate such actions to
the furthest extent possible.
Establish a Requirements Traceability Matrix
The business rules for contingencies and responsibilities can be defined
explicitly within a Requirements Traceability Matrix (RTM) or contained in a separate
document and referenced in the matrix, as the example in Table 3 illustrates. Such a
practice leaves no doubt as to responsibilities and actions under certain conditions as
they occur during the product-development phase.
The RTM functions as a sort of "chain of custody" document for requirements
and can include pointers to links from requirements to sources, as well as pointers to
business rules. For example, any given requirement must be traced back to a specified
need, be it a use case, business essential, industry-recognized standard, or

government regulation. As mentioned previously, linking requirements with sources
minimizes or even eliminates the presence of spurious or frivolous requirements that
lack any justification. The RTM is another record of mutual understanding, but also
helps during the development phase.
As software design and development proceed, the design elements and the
actual code must be tied back to the requirement(s) that define them. The RTM is
completed as development progresses; it can't be completed beforehand (see Table 3).
What Should I Know about Writing an SRS?
Unlike formal language that allows developers and designers some latitude, the
natural language of SRSs must be exact, without ambiguity and precise because the
design specification, statement of work and other project documents are what drive the
development of the final product. That final product must be tested and validated
against the design and original requirements. Specification language that allows for
interpretation of key requirements will not yield a satisfactory final product and will
likely lead to cost overruns, extended schedules and missed deliverable deadlines.
Table 4 shows the fundamental characteristics of a quality SRS.
Table 4 The 10 language quality characteristics of an SRS
SRS Quality What It Means

Characteristic
Complete SRS defines precisely all the go-live situations that will be
encountered and the system's capability to successfully address
them.
Consistent SRS capability functions and performance levels are compatible,

and the required quality features (security, reliability, etc.) do
not negate those capability functions.
Accurate SRS precisely defines the system's capability in a real-world

environment, as well as how it interfaces and interacts with it.
This aspect of requirements is a significant problem area for
many SRSs.
Modifiable The logical, hierarchical structure of the SRS should facilitate

any necessary modifications (grouping related issues together
and separating them from unrelated issues makes the SRS
easier to modify).
Ranked Individual requirements of an SRS are hierarchically arranged

according to stability, security, perceived ease/difficulty of
implementation or other parameter that helps in the design of
that and subsequent documents.
Testable An SRS must be stated in such a manner that unambiguous

assessment criteria (pass/fail or some quantitative measure)
can be derived from the SRS itself.
Traceable Each requirement in an SRS must be uniquely identified to a

source (use case, government requirement, industry standard,
etc.)
Unambiguous SRS must contain requirements statements that can be

interpreted in one way only. This is another area that creates
significant problems for SRS development because of the use of
natural language.
Valid A valid SRS is one in which all parties and project participants
can understand, analyze, accept or approve it. This is one of the
main reasons SRSs are written using natural language.
Verifiable A verifiable SRS is consistent from one level of abstraction to

another. Most attributes of a specification are subjective and a
conclusive assessment of quality requires a technical review by
domain experts. Using indicators of strength and weakness
provide some evidence that preferred attributes are or are not
present.
What makes an SRS "good?" How do we know when we've written a "quality"
specification? The most obvious answer is that a quality specification is one that fully
addresses all the customer requirements for a particular product or system. That's part
of the answer. While many quality attributes of an SRS are subjective, we do need
indicators or measures that provide a sense of how strong or weak the language is in
an SRS. A "strong" SRS is one in which the requirements are tightly, unambiguously,
and precisely defined in such away that leaves no other interpretation or meaning to
any individual requirement.
Table 5 Quality measures related to individual SRS statements
Imperatives: Words and phrases that command the presence of some feature,
function, or deliverable. They are listed below in decreasing order of strength.
Shall Used to dictate the provision of a functional capability.
Must or must Most often used to establish performance requirement or

not constraints.
Is required to Used as an imperative in SRS statements when written in

passive voice.
Are applicable Used to include, by reference, standards or other

documentation as an addition to the requirement being
specified.
Responsible for Used as an imperative in SRSs that are written for systems
with pre-defined architectures.
Will Used to cite things that the operational or development

environment is to provide to the capability being specified. For
example, The vehicle's exhaust system will power the ABC
widget.
Should Not used often as an imperative in SRS statements; however,

when used, the SRS statement always reads weak. Avoid
using Should in your SRSs.
Continuances: Phrases that follow an imperative and introduce the specification of

requirements at a lower level. There is a correlation with the frequency of use
of continuances and SRS organization and structure, up to a point. Excessive use
of continuances often indicates a very complex, detailed SRS.
The continuances below are listed in decreasing order of use within NASA SRSs.
Use continuances in your SRSs, but balance the frequency with the appropriate
level of detail called for in the SRS.
1. Below:
2. As follows:
3. Following:
4. Listed:
5. In particular:
6. Support:
Directives: Categories of words and phrases that indicate illustrative information

within the SRS. A high ratio of total number of directives to total text line count
appears to correlate with how precisely requirements are specified within the SRS.
The directives below are listed in decreasing order of occurrence within NASA
SRSs. Incorporate the use of directives in your SRSs.
1. Figure
2. Table
3. For example
4. Note
Options: A category of words that provide latitude in satisfying the SRS statements
that contain them. This category of words loosens the SRS, reduces the client's
control over the final product and allows for possible cost and schedule risks. You
should avoid using them in your SRS. The options below are listed in the order
they are found most often in NASA SRSs.
1. Can
2. May
3. Optionally
Weak phrases: A category of clauses that can create uncertainty and

multiple/subjective interpretation. The total number of weak phrases found in an
SRS indicates the relative ambiguity and incompleteness of an SRS. The weak
phrases below are listed alphabetically.
Adequate be able to easy provide for
as a minimum be capable of effective timely
as applicable but not limited if possible tbd

to
as appropriate capability of if
practical
at a minimum capability to normal

Size: Used to indicate the size of the SRS document, and is the total number of the
following:
1. Lines of text
2. Number of imperatives
3. Subjects of SRS statements
4. Paragraphs
Text Structure: Related to the number of statement identifiers found at each

hierarchical level of the SRS and indicate the document's organization,
consistency and level of detail. The most detailed NASA SRSs were nine levels
deep. High-level SRSs were rarely more than four levels deep. SRSs deemed well
organized and a consistent level of detail had text structures resembling pyramids
(few level 1 headings but each lower level having more numbered statements than
the level above it). Hour-glass-shaped text structures (many level 1 headings, few a
mid-levels, and many at lower levels) usually contain a greater amount of
introductory and administrative information. Diamond-shaped text
structures (pyramid shape followed by decreasing statement counts at levels below
the pyramid) indicated that subjects introduced at higher levels were addressed at
various levels of detail.
Specification Depth: The number of imperatives found at each of the SRS levels of
text structure. These numbers include the count of lower level list items that are
introduced at a higher level by an imperative and followed by a continuance. The
numbers provide some insight into how much of the Requirements document was
included in the SRS, and can indicate
how concise the SRS is in specifying the requirements.
Readability Statistics: Measurements of how easily an adult can read and

understand the requirements document. Four readability statistics are used
(calculated by Microsoft Word). While readability statistics provide a relative
quantitative measure, don't sacrifice sufficient technical depth in your SRS for a
number.
1. Flesch Reading Ease index
2. Flesch-Kincaid Grade Level index
3. Coleman-Liau Grade Level index
4. Bormuth Grade Level index
10. Conclusion
There's so much more we could say about requirements and specifications.
Hopefully, this information will help you get started when you are called upon--or step
up--to help the development team. Writing top-quality requirements specifications

begins with a complete definition of customer requirements. Coupled with a natural
language that incorporates strength and weakness quality indicators--not to mention
the adoption of a good SRS template--technical communications professionals well-
trained in requirements gathering, template design, and natural language use are in
the best position to create and add value to such critical project documentation.
11. Suggested readings
Hill Publications.
“Software Testing Techniques” by Boris Beizer, Dreamtech Press, New Delhi.
Publishers.
M.Sc (IT) Part-II PAPER : MS(A)-213
Software Design
1.1 Introduction: Software Design

1.2 Design Guidelines
1.3 Design Principles
1.4 Design Concepts
1.4.1 Abstraction
1.4.2 Refinement
1.4.3 Modularity
1.4.4 Software Architecture
1.4.5 Control Hierarchy
1.4.6 Structural Partitioning
1.4.7 Data Structure
1.4.8 Software Procedure
1.4.9 Information Hiding
1.5 Effective Modular Design
1.5.1 Functional Independence
1.5.2 Cohesion
1.5.3 Coupling
1.6 Summary
Objective
This lesson provides an introduction to the principles and concepts relevant to
the software design. It examines the role and context of the design activity as a form of
problem-solving process, describes how this is supported by current design methods,
and considers the strategies, strengths, limitations and main domains of application of
these methods.
1.1 Introduction: Software Design
Software design is a process of defining the architecture, components,
interfaces, and other characteristics of a system or component and planning for a
software solution. After the purpose and specifications of software is determined,
software developers will design or employ designers to develop a plan for a solution. A
software design may be platform-independent or platform-specific, depending on the
availability of the technology called for by the design. Viewed as a process, software
design is the software engineering life cycle activity in which software requirements are
analyzed in order to produce a description of the software’s internal structure that will
serve as the basis for its construction. More precisely, a software design (the result)
must describe the software architecture and the interfaces between those components.
It must also describe the components at a level of detail that enable their construction.
Software design plays an important role in developing software: it allows
software engineers to produce various models that form a kind of blueprint of the
solution to be implemented. We can analyze and evaluate these models to determine
whether or not they will allow us to fulfill the various requirements. We can also
examine and evaluate various alternative solutions and trade-offs. Finally, we can use
the resulting models to plan the subsequent development activities, in addition to
using them as input and the starting point of construction and testing.
In a standard listing of software life cycle processes such as IEEE/EIA 12207 Software
Life Cycle Processes, software design consists of two activities that fit between software
requirements analysis and software construction:
 Software architectural design (sometimes called top-level design): describing
software’s top-level structure and organization and identifying the various
components.
 Software detailed design: describing each component sufficiently to allow for its
construction.
Software Design: What is it and why is it important?
A software design is a meaningful engineering representation of some software
product that is to be built. A design can be traced to the customer's requirements and
can be assessed for quality against predefined criteria. In the software engineering
context, design focuses on four major areas of concern: data, architecture, interfaces
and components.
The design process is very important. From a practical standpoint, as a
labourer, one would not attempt to build a house without an approved blueprint
thereby risking the structural integrity and customer satisfaction. In the same manner,
the approach to building software products is no different. The emphasis in design is
on quality; this phase provides us with representation of software that can be assessed
for quality. Furthermore, this is the only phase in which the customer’s requirements
can be accurately translated into a finished software product or system. As such,
software design serves as the foundation for all software engineering steps that follow
regardless of which process model is being employed. Without a proper design we risk
building an unstable system – one that will fail when small changes are made, one that
may be difficult to test; one whose quality cannot be assessed until late in the software
process, perhaps when critical deadlines are approaching and much capital has
already been invested into the product.
During the design process the software specifications are transformed into
design models that describe the details of the data structures, system architecture,
interface and components. Each design product is reviewed for quality before moving to
the next phase of software development. At the end of the design process a design
specification document is produced. This document is composed of the design models
that describe the data, architecture, interfaces and components.
At the data and architectural levels the emphasis is placed on the patterns as
they relate to the application to be built. Whereas at the interface level, human
ergonomics often dictate the design approach employed. Lastly, at the component level
the design is concerned with a “programming approach” which leads to effective data
and procedural designs.
Design Specification Models
 Data design – created by transforming the analysis information model (data
dictionary and ERD) into data structures required to implement the software.
Part of the data design may occur in conjunction with the design of software
architecture. More detailed data design occurs as each software component is
designed.
 Architectural design - defines the relationships among the major structural
elements of the software, the “design patterns” than can be used to achieve the
requirements that have been defined for the system and the constraints that
affect the way in which the architectural patterns can be applied. It is derived
from the system specification, the analysis model and the subsystem
interactions defined in the analysis model (DFD).
 Interface design - describes how the software elements communicate with each
other, with other systems, and with human users; the data flow and control flow
diagrams provide much of the necessary information required.
 Component-level design - created by transforming the structural elements
defined by the software architecture into procedural descriptions of software
components using information obtained from the process specification (PSPEC),
control specification (CSPEC), and state transition diagram (STD).
These models collectively form the design model, which is represented
diagrammatically as a pyramid structure with data design at the base and component
level design at the pinnacle. Note that each level produces its own documentation,
which collectively form the design specifications document, along with the guidelines
for testing individual modules and the integration of the entire package. Algorithm
description and other relevant information may be included as an appendix.
1.2 Design Guidelines

In order to evaluate the quality of a design (representation) the criteria for a good
design should be established. Such a design should:
 exhibit good architectural structure
 be modular
 contain distinct representations of data, architecture, interfaces, and
components (modules)
 lead to data structures that are appropriate for the objects to be implemented
and be drawn from recognizable design patterns
 lead to components that exhibit independent functional characteristics
 lead to interfaces that reduce the complexity of connections between modules
and with the external environment
 be derived using a reputable method that is driven by information obtained
during software requirements analysis
These criteria are not achieved by chance. The software design process encourages
good design through the application of fundamental design principles, systematic
methodology and through review.
1.3 Design Principles
Software design can be viewed as both a process and a model. “The design
process is a sequence of steps that enable the designer to describe all aspects of the
software to be built. However, it is not merely a cookbook; for a competent and
successful design, the designer must use creative skill, past experience, a sense of
what makes “good” software and have a commitment to quality.
The design model is equivalent to the architect’s plans for a house. It begins by
representing the totality of the entity to be built (e.g. a 3D rendering of the house), and
slowly refines the entity to provide guidance for constructing each detail (e.g. the
plumbing layout). Similarly the design model that is created for software provides a
variety of views of the computer software.” – adapted from book by R Pressman.
The set of principles which has been established to aid the software engineer in
navigating the design process are:
1. The design process should not suffer from tunnel vision – A good designer
should consider alternative approaches. Judging each based on the
requirements of the problem, the resources available to do the job and any other
constraints.
2. The design should be traceable to the analysis model – because a single
element of the design model often traces to multiple requirements, it is
necessary to have a means of tracking how the requirements have been satisfied
by the model
3. The design should not reinvent the wheel – Systems are constructed using a
set of design patterns, many of which may have likely been encountered before.
These patterns should always be chosen as an alternative to reinvention. Time
is short and resources are limited! Design time should be invested in
representing truly new ideas and integrating those patterns that already exist.
4. The design should minimise intellectual distance between the software and
the problem as it exists in the real world – That is, the structure of the
software design should (whenever possible) mimic the structure of the problem
domain.
5. The design should exhibit uniformity and integration – a design is uniform if
it appears that one person developed the whole thing. Rules of style and format
should be defined for a design team before design work begins. A design is
integrated if care is taken in defining interfaces between design components.
6. The design should be structured to degrade gently, even with bad data,
events, or operating conditions are encountered – Well-designed software
should never “bomb”. It should be designed to accommodate unusual
circumstances, and if it must terminate processing, do so in a graceful manner.
7. The design should be reviewed to minimize conceptual (semantic) errors –
there is sometimes the tendency to focus on minute details when the design is
reviewed, missing the forest for the trees. The designer team should ensure that
major conceptual elements of the design have been addressed before worrying
about the syntax if the design model.
8. Design is not coding, coding is not design – Even when detailed designs are
created for program components, the level of abstraction of the design model is
higher than source code. The only design decisions made of the coding level
address the small implementation details that enable the procedural design to
be coded.
9. The design should be structured to accommodate change
10. The design should be assessed for quality as it is being created
When these design principles are properly applied, the design exhibits both external
and internal quality factors. External quality factors are those factors that can readily
be observed by the user, (e.g. speed, reliability, correctness, usability). Internal quality
factors relate to the technical quality (which is important to the software engineer)
more so the quality of the design itself. To achieve internal quality factors the designer
must understand basic design concepts.
1.4 Design Concepts
A set of fundamental software design concepts has evolved over the past four
decades. Although the degree of interest in each concept has varied over the years,
each has stood the test of time. Each provides the software designer with a foundation
from which more sophisticated design methods can be applied. Each helps the
software engineer to answer the following questions:
 What criteria can be used to partition software into individual components?
 How function or data is structure detail separated from a conceptual
representation of the software?
 What uniform criteria define the technical quality of a software design?
M. A. Jackson once said: "The beginning of wisdom for a [software engineer] is to
recognize the difference between getting a program to work and getting it right".
Fundamental software design concepts provide the necessary framework for "getting it
right."
1.4.1 Abstraction
When we consider a modular solution to any problem, many levels of
abstraction can be posed. At the highest level of abstraction, a solution is stated in
broad terms using the language of the problem environment. At lower levels of
abstraction, a more detailed description of the solution is provided.
As we move through different levels of abstraction, we work to create procedural
and data abstractions. A procedural abstraction is a named sequence of instructions
that has a specific and limited function. An example of a procedural abstraction would
be the word open for a door. Open implies a long sequence of procedural steps (e.g.,
walk to the door, reach out and grasp knob, turn knob and pull door, step away from
moving door, etc.).
A data abstraction is a named collection of data that describes a data object. In
the context of the procedural abstraction open, we can define a data abstraction called
door. Like any data object, the data abstraction for door would encompass a set of
attributes that describe the door (e.g., door type, swing direction, opening mechanism,
weight, dimensions). It follows that the procedural abstraction open would make use of
information contained in the attributes of the data abstraction door.
1.4.2 Refinement
Stepwise refinement is a top-down design strategy originally proposed by
Niklaus Wirth. A program is developed by successively refining levels of procedural
detail. A hierarchy is developed by decomposing a macroscopic statement of function (a
procedural abstraction) in a stepwise fashion until programming language statements
are reached.
Refinement is actually a process of elaboration. We begin with a statement of
function (or description of information) that is defined at a high level of abstraction.
That is, the statement describes function or information conceptually but provides no
information about the internal workings of the function or the internal structure of the
information. Refinement causes the designer to elaborate on the original statement,
providing more and more detail as each successive refinement (elaboration) occurs.
Abstraction and refinement are complementary concepts. Abstraction enables a
designer to specify procedure and data and yet suppress low-level details. Refinement
helps the designer to reveal low-level details as design progresses. Both concepts aid
the designer in creating a complete design model as the design evolves.
1.4.3 Modularity
The concept of modularity in computer software has been espoused for almost
five decades. Software architecture embodies modularity; that is, software is divided
into separately named and addressable components, often called modules that are
integrated to satisfy problem requirements.
It has been stated that "modularity is the single attribute of software that allows
a program to be intellectually manageable". Monolithic software (i.e., a large program
composed of a single module) cannot be easily grasped by a reader. The number of
control paths, span of reference, number of variables and overall complexity would
make understanding close to impossible.
An important question arises when modularity is considered. How do we define
an appropriate module of a given size? The answer lies in the method(s) used to define
modules within a system. Meyer defines five criteria that enable us to evaluate a design
method with respect to its ability to define an effective modular system:
Modular decomposability. If a design method provides a systematic mechanism for
decomposing the problem into subproblems, it will reduce the complexity of the overall
problem, thereby achieving an effective modular solution.
Modular composability. If a design method enables existing (reusable) design
components to be assembled into a new system, it will yield a modular solution that
does not reinvent the wheel.
Modular understandability. If a module can be understood as a standalone unit

(without reference to other modules), it will be easier to build and easier to change.
Modular continuity. If small changes to the system requirements result in changes to
individual modules, rather than system wide changes, the impact of change-induced
side effects will be minimized.
Modular protection. If an aberrant condition occurs within a module and its effects
are constrained within that module, the impact of error-induced side effects will be
minimized.
Finally, it is important to note that a system may be designed modularly, even if
its implementation must be "monolithic." There are situations (e.g., real-time software,
embedded software) in which relatively minimal speed and memory overhead
introduced by subprograms (i.e., subroutines, procedures) is unacceptable. In such
situations, software can and should be designed with modularity as an overriding
philosophy. Code may be developed "in-line." Although the program source code may
not look modular at first glance, the philosophy has been maintained and the program
will provide the benefits of a modular system.
1.4.4 Software Architecture
Software architecture alludes to “the overall structure of the software and the
ways in which that structure provides conceptual integrity for a system”. In its simplest
form, architecture is the hierarchical structure of program components (modules), the
manner in which these components interact and the structure of data that are used by
the components. In a broader sense, however, components can be generalized to
represent major system elements and their interactions.
One goal of software design is to derive an architectural rendering of a system. This
rendering serves as a framework from which more detailed design activities are
conducted. A set of architectural patterns enable a software engineer to reuse design
level concepts.
1.4.5 Control Hierarchy
Control hierarchy, also called program structure, represents the organization of
program components (modules) and implies a hierarchy of control. It does not
represent procedural aspects of software such as sequence of processes, occurrence or
order of decisions or repetition of operations; nor is it necessarily applicable to all
architectural styles.
Different notations are used to represent control hierarchy for those
architectural styles that are amenable to this representation. The most common is the
treelike diagram that represents hierarchical control for call and return architectures.
1.4.6 Structural Partitioning
If the architectural style of a system is hierarchical, the program structure can
be partitioned both horizontally and vertically. Referring to Figure, horizontal
partitioning defines separate branches of the modular hierarchy for each major
program function. Control modules, represented in a darker shade are used to

coordinate communication between and execution of the functions. The simplest
approach to horizontal partitioning defines three partitions—input, data
transformation (often called processing) and output. Partitioning the architecture
horizontally provides a number of distinct benefits:
 software that is easier to test
 software that is easier to maintain
 propagation of fewer side effects
 software that is easier to extend
Because major functions are decoupled from one another, change tends to be
less complex and extensions to the system (a common occurrence) tend to be easier to
accomplish without side effects. On the negative side, horizontal partitioning often
causes more data to be passed across module interfaces and can complicate the overall
control of program flow (if processing requires rapid movement from one function to
another).
Vertical partitioning (Figure b), often called factoring, suggests that control
(decision making) and work should be distributed top-down in the program structure.
Top level modules should perform control functions and do little actual processing
work. Modules that reside low in the structure should be the workers, performing all
input, computation, and output tasks.
The nature of change in program structures justifies the need for vertical
partitioning. Referring to Figure b, it can be seen that a change in a control module
(high in the structure) will have a higher probability of propagating side effects to
modules that are subordinate to it. A change to a worker module, given its low level in
the structure, is less likely to cause the propagation of side effects. In general, changes
to computer programs revolve around changes to input, computation or
transformation, and output. The overall control structure of the program (i.e., its basic
behavior is far less likely to change). For this reason vertically partitioned structures
are less likely to be susceptible to side effects when changes are made and will
therefore be more maintainable—a key quality factor.
1.4.7 Data Structure
Data structure is a representation of the logical relationship among individual
elements of data. Because the structure of information will invariably affect the final
procedural design, data structure is as important as program structure to the
representation of software architecture. Data structure dictates the organization,
methods of access, degree of associativity and processing alternatives for information.
The organization and complexity of a data structure are limited only by the ingenuity of
the designer. There are, however, a limited number of classic data structures that form
the building blocks for more sophisticated structures.
A scalar item is the simplest of all data structures. As its name implies, a scalar
item represents a single element of information that may be addressed by an identifier;
that is, access may be achieved by specifying a single address in memory. The size and
format of a scalar item may vary within bounds that are dictated by a programming
language. For example, a scalar item may be a logical entity one bit long, an integer or
floating point number that is 8 to 64 bits long, or a character string that is hundreds
or thousands of bytes long.
When scalar items are organized as a list or contiguous group, a sequential
vector is formed. Vectors are the most common of all data structures and open the
door to variable indexing of information.
When the sequential vector is extended to two, three and ultimately, an
arbitrary number of dimensions, an n-dimensional space is created. The most common
n-dimensional space is the two-dimensional matrix. In many programming languages,
an ndimensional space is called an array.
Items, vectors, and spaces may be organized in a variety of formats. A linked list
is a data structure that organizes noncontiguous scalar items, vectors, or spaces in a
manner (called nodes) that enables them to be processed as a list. Each node contains
the appropriate data organization (e.g., a vector) and one or more pointers that indicate
the address in storage of the next node in the list. Nodes may be added at any point in
the list by redefining pointers to accommodate the new list entry.
Other data structures incorporate or are constructed using the fundamental
data structures just described. For example, a hierarchical data structure is
implemented using multilinked lists that contain scalar items, vectors and possibly, n-
dimensional spaces. A hierarchical structure is commonly encountered in applications

that require information categorization and associativity.
It is important to note that data structures, like program structure, can be
represented at different levels of abstraction. For example, a stack is a conceptual
model of a data structure that can be implemented as a vector or a linked list.
Depending on the level of design detail, the internal workings of a stack may or may
not be specified.
1.4.8 Software Procedure
Program structure defines control hierarchy without regard to the sequence of
processing and decisions. Software procedure focuses on the processing details of each
module individually. Procedure must provide a precise specification of processing,
including sequence of events, exact decision points, repetitive operations and even data
organization and structure.
There is, of course, a relationship between structure and procedure. The
processing indicated for each module must include a reference to all modules
subordinate to the module being described. That is, a procedural representation of
software is layered as illustrated in Figure below.
1.4.9 Information Hiding

The concept of modularity leads every software designer to a fundamental
question: "How do we decompose a software solution to obtain the best set of
modules?" The principle of information hiding suggests that modules be "characterized
by design decisions that (each) hides from all others." In other words, modules should
be specified and designed so that information (procedure and data) contained within a
module is inaccessible to other modules that have no need for such information.
Hiding implies that effective modularity can be achieved by defining a set of
independent modules that communicate with one another only that information
necessary to achieve software function. Abstraction helps to define the procedural (or
informational) entities that make up the software. Hiding defines and enforces access
constraints to both procedural detail within a module and any local data structure
used by the module. The use of information hiding as a design criterion for modular
systems provides the greatest benefits when modifications are required during testing
and later, during software maintenance. Because most data and procedure are hidden
from other parts of the software, inadvertent errors introduced during modification are
less likely to propagate to other locations within the software.
1.5 Effective Modular Design
All the fundamental design concepts described in the preceding section serve to
precipitate modular designs. In fact, modularity has become an accepted approach in
all engineering disciplines. A modular design reduces complexity, facilitates change (a
critical aspect of software maintainability) and results in easier implementation by
encouraging parallel development of different parts of a system.
1.5.1 Functional Independence
The concept of functional independence is a direct outgrowth of modularity and
the concepts of abstraction and information hiding. In landmark papers on software
design Parnas and Wirth allude to refinement techniques that enhance module
independence. Later work by Stevens, Myers and Constantine solidified the concept.
Functional independence is achieved by developing modules with "single-minded"
function and an "aversion" to excessive interaction with other modules. Stated another
way, we want to design software so that each module addresses a specific subfunction
of requirements and has a simple interface when viewed from other parts of the
program structure. It is fair to ask why independence is important. Software with
effective modularity, that is, independent modules, is easier to develop because
function may be compartmentalized and interfaces are simplified (consider the
ramifications when development is conducted by a team). Independent modules are
easier to maintain (and test) because secondary effects caused by design or code
modification are limited, error propagation is reduced, and reusable modules are
possible. To summarize, functional independence is a key to good design and design is
the key to software quality.
Independence is measured using two qualitative criteria: cohesion and coupling.
Cohesion is a measure of the relative functional strength of a module. Coupling is a
measure of the relative interdependence among modules.
1.5.2 Cohesion
Cohesion is a natural extension of the information hiding concept. A
cohesive module performs a single task within a software procedure, requiring little
interaction with procedures being performed in other parts of a program. Stated
simply, a cohesive module should (ideally) do just one thing. Cohesion may be
represented as a "spectrum." We always strive for high cohesion, although the mid-
range of the spectrum is often acceptable. The scale for cohesion is nonlinear. That is,
low-end cohesiveness is much "worse" than middle range, which is nearly as "good" as
high-end cohesion. In practice, a designer need not be concerned with categorizing
cohesion in a specific module. Rather, the overall concept should be understood and
low levels of cohesion should be avoided when modules are designed.
At the low (undesirable) end of the spectrum, we encounter a module that
performs a set of tasks that relate to each other loosely, if at all. Such modules are
termed coincidentally cohesive. A module that performs tasks that are related logically
(e.g., a module that produces all output regardless of type) is logically cohesive. When a
module contains tasks that are related by the fact that all must be executed with the
same span of time, the module exhibits temporal cohesion.
As an example of low cohesion, consider a module that performs error
processing for an engineering analysis package. The module is called when computed
data exceed prespecified bounds. It performs the following tasks: (1) computes
supplementary data based on original computed data, (2) produces an error report
(with graphical content) on the user's workstation, (3) performs follow-up calculations
requested by the user, (4) updates a database, and (5) enables menu selection for
subsequent processing. Although the preceding tasks are loosely related, each is an
independent functional entity that might best be performed as a separate module.
Combining the functions into a single module can serve only to increase the likelihood
of error propagation when a modification is made to one of its processing tasks.
Moderate levels of cohesion are relatively close to one another in the degree of
module independence. When processing elements of a module are related and must be
executed in a specific order, procedural cohesion exists. When all processing elements
concentrate on one area of a data structure, communicational cohesion is present.
High cohesion is characterized by a module that performs one distinct procedural task.
As we have already noted, it is unnecessary to determine the precise level of cohesion.
Rather it is important to strive for high cohesion and recognize low cohesion so that
software design can be modified to achieve greater functional independence.
1.5.3 Coupling
Coupling is a measure of interconnection among modules in a software
structure. Coupling depends on the interface complexity between modules, the point
at which entry or reference is made to a module, and what data pass across the
interface. In software design, we strive for lowest possible coupling. Simple connectivity
among modules results in software that is easier to understand and less prone to a
"ripple effect”, caused when errors occur at one location and propagate through a
system.
Figure: Types of Coupling

Figure provides examples of different types of module coupling. Modules a and d
are subordinate to different modules. Each is unrelated and therefore no direct
coupling occurs. Module c is subordinate to module a and is accessed via a
conventional argument list, through which data are passed. As long as a simple
argument list is present (i.e., simple data are passed; a one-to-one correspondence of
items exists), low coupling (called data coupling) is exhibited in this portion of
structure. A variation of data coupling, called stamp coupling, is found when a portion
of a data structure (rather than simple arguments) is passed via a module interface.
This occurs between modules b and a. At moderate levels, coupling is characterized by
passage of control between modules. Control coupling is very common in most software
designs and is shown in Figure where a “control flag” (a variable that controls decisions
in a subordinate or superordinate module) is passed between modules d and e.
Relatively high levels of coupling occur when modules are tied to an environment
external to software. For example, I/O couples a module to specific devices, formats,
and communication protocols. External coupling is essential, but should be limited to
a small number of modules with a structure. High coupling also occurs when a
number of modules reference a global data area. Common coupling, as this mode is
called, is shown in Figure. Modules c, g, and k each access a data item in a global data
area (e.g., a disk file or a globally accessible memory area). Module c initializes the
item. Later module g recomputes and updates the item. Let's assume that an error
occurs and g updates the item incorrectly. Much later in processing module, k reads
the item, attempts to process it and fails, causing the software to abort. The apparent
cause of abort is module k; the actual cause, module g. Diagnosing problems in
structures with considerable common coupling is time consuming and difficult.
However, this does not mean that the use of global data is necessarily "bad." It does
mean that a software designer must be aware of potential consequences of common
coupling and take special care to guard against them.
The highest degree of coupling, content coupling, occurs when one module
makes use of data or control information maintained within the boundary of another
module. Secondarily, content coupling occurs when branches are made into the middle
of a module. This mode of coupling can and should be avoided. The coupling modes
just discussed occur because of design decisions made when structure was developed.
Variants of external coupling, however, may be introduced during coding. For example,
compiler coupling ties source code to specific (and often nonstandard) attributes of a
compiler; operating system (OS) coupling ties design and resultant code to operating
system "hooks" that can create havoc when OS changes occur.
1.6 Summary
In this lesson we have discussed the principles and concepts relevant to the
software design. It examines the role and context of the design activity as a form of
problem-solving process, describes how this is supported by current design methods,
and considers the strategies, strengths, limitations, and main domains of application of
these methods.
Hill Publications.
“Software Testing Techniques” by Boris Beizer, Dreamtech Press, New Delhi.
Publishers.
M.Sc (IT) PART-II PAPER : MS(A)-213
DFD and UML
1. Data flow diagram

2. Data dictionary
3. Object Modelling Using UML
4. Advantages of OOD
1. Data flow diagrams

The Data Flow Diagram is also known as a Data Flow Graph or a Bubble
Chart. A DFD serve the purpose of clarifying system requirements and identifying
major transformations. DFDs show the flow of data through a system. It is an
important modeling tool that allows us to picture a system as a network of functional
process.
Data flow diagrams are well known and widely used notations for specifying the
functions of an information system. They describe system as collections of data that
are manipulated by functions. Data can be organized in several ways: they can be
stored in data repositories, they can flow in data flows and they can be transformed to
or from the external environment.
One of the reasons for the success of DFD is that they can be expressed by
means of an attractive graphical notation that makes them easy to use.
Symbols used for constructing DFDs
There are different types of symbols used to construct DFDs. The meaning of
each symbol is explained below:
1. Functional symbol:
A Function is represented using a circle. This symbol is called a process or a
bubble or performs some processing of input data.
2. External entity
A square defines a source of destination of system data. External entities
represent any entity that supplies or receives information from the system but is not a
part of the system.
3. Data flow symbol

A directed arc or arrow is used as a data flow symbol. A data flow symbol
represents the data flow occurring between two processes or between an external entity
and a process in the direction of data flow arrow.
4. Data store symbol

A data store symbol is represented using two parallel lines. A logical file can
represent either a data store symbol, which can represent either a data structure or a
physical file on the disk. Each data store is connected to a process by means of a data
flow symbol. The direction of data flow arrow shows whether data is being read from or
written into a data store.
5. Output symbol
It is used to represent data acquisition and production during human computer
interaction.
Example of DFD
Figure 1 shows how the symbols can be composed to form a DFD. The DFD
describes the arithmetic expression
(a+b)*(c+a+d)
Assuming that the data a,b,c and d are read from a terminal and the result is
printed. The figure shows that the arrow can be “forked” to represent the fact that the
same datum is used in different places.
Figure 1
Figure 2 describes a simplified information system for a public library. The data
and functions shown are not necessarily computer data and computer functions. The
DFD describes physical objects, such as books and shelves together with data stores
that are likely to be but not necessarily realized as computer files. Getting a book from
the shelf can be done either automatically by a robot or manually. In both cases the
action of getting a book is represented by a function depicted by a bubble. The figure
could even represent the organization of a library with no computerized procedures.
Figure 2
Figure describes the fact that in order to obtain a book, the following are
necessary: an explicit user request consisting of the title and the name of the author of
the book and the users name; access to the shelves that contain the books; a list of
authors; and a list of titles. These provide the information necessary to find the book.
General guidelines and rules for constructing DFD
The following guidelines will help avoid constructing DFDs that are quite simply
wrong or incomplete.
 Remember that a DFD is not a flow chart.
 All names should be unique.
 Processes are always running, they do not start or stop.
 All data flows are named.
 Do numbering of processes
 Avoid complex DFD (if possible)
 The DFD should be internally consistent.
 Keep a note of all the processes and external entities. Give unique names to
them. Identify the manner in which they interact with each other.
 Every process should have minimum of one input and one output.
 The direction of flow is from source to destination.
 Only data needed to perform the process should be an input to the process.
2. Data dictionary
Every DFD model of a system must be accompanied by a data dictionary. A data
dictionary lists all data items appearing in the DFD model of a system. The data items
listed include all data flows and the contents of all data stores appearing on all the
DFDs in the DFD model of a system.
A data dictionary lists the purpose of all data items and the definition of all
composite data items In terms of their component data items. For example, a data
dictionary entry may represent that the data grossPay consists of the components
regularPay and overtimePay.
grossPay = regularPay + overtimePay
For the smallest units of data items, the data dictionary lists their name and their
type. The operators using a composite data item can be expressed in terms of their
component data items, and these are discussed in the next subsection.
 A data dictionary provides a standard terminology for all relevant data for use
by all engineers working in the same project. A consistent vocabulary or data
items is very important, since in large projects different engineers have a
tendency to use different terms to refer to the same data, which unnecessarily
causes confusion.
 The data dictionary provides the analyst with a means to determine the
definition of different data structures in terms of their component elements.
For large systems, the data dictionary can be extremely complex and
voluminous. Even moderate-sized projects can have thousands of entries in the data
dictionary. It becomes extremely difficult to maintain a voluminous dictionary
manually. Computer Aided Software Engineering (CASE) tools come handy to overcome
this problem. Most CASE tools usually capture the data items appearing in a DFD and
automatically generate the data dictionary. These tools also support some query
language facility to raise queries about the definition and usage of data items. For
example, queries may be formulated either to determine which data item affects which
processes, which process affects which data items or to find the definition and usage of
specific data items and so on. Query handling is facilitated by storing the data
dictionary in a Relational Database Management System (RDBMS).
3. Object Modelling Using UML
The object-oriented software development style has become extremely popular.
Since its inception in the early 1980s, the object technology has made rapid progress.
The development of this technology gathered momentum in the 1990s and is now
nearing maturity. In this Lesson, we will review some basic concepts in object-
orientation. We will then discuss UML (Unified Modelling Language) which is poised to
become a standard in modelling object-oriented systems.
Before starting our discussion on object modelling using UML, we will first
briefly review some basic concepts that are central to the object-oriented approach.
Overview of Object-Oriented Concepts
Some important concepts and terms related to the object-oriented approach are
pictorially shown in Figure 3. We will first discuss the basic mechanisms shown in the
figure, namely objects, classes, inheritance, messages and methods. We will then
discuss some key concepts and examine a few related technical terms.
Figure 3: basic concepts used in object-oriented approach
Basic Mechanisms
The following are some of the basic mechanisms used in the object-oriented paradigm.
Objects
In the object-oriented approach, a system is designed as a set of interacting
objects, Normally, each object represents a tangible real-world entity such as a
library member, an employee, a book, etc. However, at times we would also consider
some conceptual entities as objects (e.g. a scheduler, a controller, etc.) to simplify
solution to certain problems.
When a system is analyzed, developed and implemented in terms of the
natural objects occurring in it, it becomes easier to understand the design and
implementation of the system. Each object essentially consists of some data that are
private to the object and a set of functions (or operations) that operate on those data
(see Figure 4). In fact, the functions of an object have the sole authority to operate on
the private data of that object. Therefore, an object cannot directly access the data
internal to another object. However, an object can indirectly access the internal data
of other objects by invoking the operations (i.e. methods) supported by those objects.
This mechanism is popularly known as the data abstraction principle. This is an
important principle that is elaborated below.
Figure 4: A model of an object

Data abstraction means that each object hides from other objects the exact way in
which its internal information is organized and manipulated. It only provides a set of
methods, which other objects can use for accessing and manipulating this private
information of the object. For example, a stack object might store its internal data
either in the form of an array of values or in the form of a linked list. Other objects
would not know how exactly this object has stored its data and how it manipulates its
data. What they would know is the set of methods such as push, pop, and top-of-stack
that it provides to the other objects for accessing and manipulating the data. An
important advantage of the principle of data abstraction is that it reduces coupling
among the objects, decreases the overall complexity of a design and helps in
maintenance and code reuse.
Each object essentially possesses certain information and supports some
operations on this information. For example, in a word processing application, a
paragraph, a line, and a page can be objects. A LibraryMember can be an object of a
library automation system. The private data of such a library member object can be:
 Name of the member
 Membership number
 Address
 Phone number
 E-mail number
 Date when admitted as a member

 Membership expiry date
 Books outstanding, etc
The operations supported by a LibraryMember object can be:
 Issue-books
 find-books-outstanding
 find-books-overdue
 return-book
 find-membership-details, etc.
The data internal to an object are often called the attributes of the object and the
functions supported by an object are called its methods. A word of caution here-the
term object is often used vaguely in the literature and also in this text. Sometimes an
object means a single entity, other times it refers to a group of similar objects. In this
text, usually the context would resolve the ambiguity, if any.
Class
Similar objects constitute a class. This means, objects possessing similar
attributes and displaying similar behavior constitute a class. For example, the set of all
employees can constitute a class in an employee pay-roll system, since each employee
object has similar attributes such as his name, code number, salary, address, etc. and
exhibits similar behavior as other employee objects. Once we define a class it serves as
a template for object creation. Since each object is created as an instance of some
class, classes can be considered as abstract data types (ADTs).
Methods and messages
The operations supported by an object are called its methods. Thus operations
and methods are almost identical terms, except for a minor technical difference.
Methods are the only mean available to other objects for accessing and manipulating
the data of another object. The methods of an object are invoked by sending messages
to it. The set of valid messages to an object constitutes its protocol.
Inheritance
The inheritance feature allows us to define a new class by extending or modifying
an existing class. The original class is called the base class (or superclass) and the
new class obtained through inheritance is called the derived class (or subclass). A
base class is a generalization of its derived classes. This means that the base class
contains only those properties that are common to all the derived classes. We can also
say that each derived class is a specialization of its base class because it modifies or
extends the basic properties of the base class in certain ways. Thus, the inheritance
relationship can be viewed as a generalization-specialization relationship. Using the
inheritance relationship, different classes can be arranged in a class hierarchy (or class
tree).
In addition to inheriting all the properties of the base class, a derived class can
define new properties. That is, it can define new data and methods. It can even give
new definitions to methods which already exist in the base class. Redefinition of the
methods which existed in the base class is called method overriding.
In Figure 5, LibraryMember is the base class for the derived classes Faculty,
Student, and Staff. Similarly, Student is the base class for the derived classes
Undergrad, postgrad, and Research. Each derived class inherits all the data and
methods of the base class. It can also define additional data and methods or modify
some of the inherited data and methods. The different classes in a library automation
system and the inheritance relationship among them are shown in Figure 5. The
inheritance relationship has been represented in this figure using a directed arrow
drawn from a derived class to its base class. In Figure 5, the LibraryMember base class
might define the data for name, address and library membership number for each
member. Though faculty, student and staff classes inherit these data, they might have
to redefine the respective issue-book methods because the number of books that can
be borrowed and the duration of loan may be different for the different category of
library members. Thus, the issue-book method is overridden by each of the derived
classes and the derived classes might define additional data max-number-books and
max-duration-of-issue which may vary for the different member categories.
Figure 5: Library information system example

Inheritance is a basic mechanism that all object-oriented languages support. In
fact, the languages that support classes (ADTs) but do not support inheritance are not
called object oriented and instead called object-based languages. But, why do we need
the inheritance relationship in the first place? An important advantage of the
inheritance mechanism is code reuse. If certain methods or data are similar in a set of
classes, then instead of defining these methods and data in each of these classes
separately, these methods and data are defined only once in the base class and then
inherited by each of its subclasses. For example, in the Library Information System
example of Figure 5, each category of member objects Faculty, Student and Staff need
the data member-name, member-address and membership-number and therefore
these data are defined in the base class LibraryMember and inherited by its
subclasses. Another advantage of the inheritance mechanism is the conceptual
simplification that comes from reducing the number of independent features of the
classes.
Multiple inheritance
Multiple inheritance is a mechanism by which a subclass can inherit attributes
and methods from more than one base class. Suppose research students are also
allowed to be staff of the institute, then some of the characteristics of the Research
class might be similar to the Student class and some other characteristics might be
similar to the Staff class. We can represent such a class hierarchy as in Figure 6.
Multiple inheritance is represented by arrows drawn from the subclass to each of the
base classes. Observe that the class Research inherits features from both the classes,
that is, Student and Staff.
Figure 6: Library information system example with multiple inheritance
Abstract class
Classes that are not intended to produce instances of themselves are called
abstract classes. Abstract classes merely exist so that behavior common to a variety of
classes can be factored into one common location, where they can be defined once.
This way code reuse can be enhanced and the effort required to develop the software
brought down. Abstract classes usually support generic methods, but the subclasses of
the abstract classes are expected to provide specific implementations of these methods.
Key concepts
We will now discuss some key concepts used in the object-oriented approaches.
Abstraction
Let us first examine how exactly the abstraction mechanism works. Abstraction
is the selective examination of certain aspects of a problem while ignoring the
remaining aspects of the problem. In other words, the main purpose of abstraction is to
consider only those aspects of the problem that are relevant for certain purpose and to
suppress aspects of the problem that are not relevant for the given purpose. Thus, the
abstraction mechanism allows us to represent a problem in a simpler way by omitting
unimportant details. Many different abstractions of the same problem are possible
depending on the purpose for which they are made. Abstraction not only helps the
development engineers to understand and appreciate the problem better, it also leads
to better comprehension of the system design by the end-users and the maintenance
team. Abstraction is supported at two levels in an object-oriented design (OOD).
 A class hierarchy can be viewed as defining an abstraction level, where each
base class is an abstraction of its subclasses.
 An object itself can be looked upon as a data abstraction entity, because it
abstracts out the exact way in which various private data items of the object
are stored and provides only a set of well-defined methods to other objects to
access and manipulate these data items.
Abstraction is a powerful mechanism for reducing complexity of software. In fact, it
has been shown from data collected from several software development projects that
software productivity is inversely proportional to software complexity. Therefore,
abstraction can be viewed as a way of increasing software productivity.
Encapsulation
The property of an object by which it interfaces with the outside world only
through messages is referred to as encapsulation. The data of an object are
encapsulated within the methods and are available only through message-based
communication. This concept is schematically represented in Figure 7.
Encapsulation offers three important advantages:
 It protects an object's variables from corruption by other objects. This protection
is provided against unauthorized access and against different types of problems
that arise from concurrent access of data such as deadlock and inconsistent
values.
 Encapsulation hides the internal structure of an object, making interaction with
the object simple and standardized. This facilitates reuse of objects across
different projects. Furthermore, if the internal structure or procedures of an
object are modified other objects are not affected and this result in easy
maintenance and bug correction.
Figure 7: Schematic representation of the concept of encapsulation

 Since objects communicate among each other using messages only, they are
weakly coupled. The fact that objects are inherently weakly coupled enhances
understandability of design since each object can be studied and understood
almost in isolation from other objects
Polymorphism
Polymorphism literally means poly (many) morphism (forms). Broadly speaking,
polymorphism denotes the following:
 The same message can result in different actions when received by different
objects. This is also referred to as static binding. This occurs when multiple
methods with the same operation name exist.
 When we have an inheritance hierarchy, an object can be assigned to another
object of its ancestor class. When such an assignment occurs, a method call to
the ancestor object would result in the invocation of the appropriate method of
the object of the derived class. Since, the exact method to which a method call
would be bound cannot be known at compile time and is dynamically decided at
the runtime, this is also known as dynamic binding.
An example of static binding is the following. Suppose a class named Circle has three
definitions for the create operation. One definition does not take any argument and
creates a circle with default parameters. The second definition takes the centre point
and radius as its parameters. In this case, the fill style values for the circle would be
set to the default "no fill". The third takes the centre point, the radius and the fill style
as its input. When the create method is invoked, depending on the parameters given in
the invocation, the appropriate method will be called. If create is invoked with no
parameters, then a default circle would be created. If only the centre and radius are
supplied, then an appropriate circle would be created with no fill type and so on. A
class definition of the Circle class with the overloaded create method is shown in
Figure 8. When the same operation (e.g. create) is implemented by multiple methods,
the method name is said to be overloaded (Notice the difference between an operation
and a method.)
class Circle {
private float x, y, radius;
private int tillType;
public create( );
public create(float x, float y, float centre);
public create(float x, float y, float centre, int fillType);
}
Figure 8: Circle class with overloaded create method
Now let us consider the concept of dynamic binding. Using dynamic binding a
programmer can send a generic message to a set of objects which may be of different
types (i.e. belonging to different classes) and leave the exact way in which the message
would be handled to the receiving objects. Suppose we have a class hierarchy of
different geometric objects as shown in Figure 9. Now, suppose the display method is
declared in
Figure 9: Class hierarchy of geometric objects

the shape class and is overridden in each derived class. If the different types of
geometric objects making up a drawing are stored in an array of type shape, then a
single call to the display method for each object would take care to display the
appropriate drawing element. That is the same draw call to a shape object would take
care of drawing the appropriate shape. This is shown in the code segment in Figure 10.
The main advantage of dynamic binding is that it leads to elegant programming and
facilitates code reuse and maintenance. With dynamic binding, new derived objects can
be added with minimal changes to existing objects. We shall illustrate this advantage of
polymorphism by comparing the code segments of an object-oriented program with a
traditional program for drawing various graphic objects on the screen. Assume that
shape is the base class and the classes circle, rectangle and ellipse are derived from it.
Now, shape can be assigned any objects of type circle, rectangle, ellipse, etc. But a
draw method invocation of the shape object would invoke the appropriate method.
Traditional code Object-oriented code

If (shape==Circle) then
draw_circle ( ); Shape.draw ( );
else if (shape==Rectangle) then
draw_rectangle ( );
---
---
---
Figure 10: Traditional code and object oriented code using dynamic binding
It can be easily seen from Figure 10 that because of dynamic binding, the
object-oriented code is much more concise and intellectually appealing. Also, suppose
in the example program segment, it is later found necessary to handle a new graphics
drawing primitive, say ellipse. Then, the procedural code has to be changed by adding
a new if then-else clause. However, in case of the object-oriented program, the code
need not change, only a new class called ellipse has to be defined.
Composite objects
Objects which contain other objects are called composite objects. Containment
may be achieved by including the pointer to one object as a value in another object or
by creating instances of the component objects in the composite object. Composite
objects can be used to realize complex behavior. Composition can occur in a hierarchy
of levels. That is, an object contained in another object may itself be a composite
object. Typically, the structures that are built up using composite objects are limited to
a tree hierarchy, i.e. no circular inclusion relation is allowed. This means that an object
cannot contain an object of its own type. It is important to bear in mind the distinction
between an inheritance hierarchy of an object class and the containment hierarchy of
object instances. The two are not related, the use of inheritance simply allows many
different object types to be defined with minimum effort by making use of the similarity
of their features. On the other hand, the use of containment allows construction of
complex objects.
Genericity
Genericity is the ability to parameterize class definitions. For example, while
defining a class stack of different types of elements such as integer stack, character
stack, and floatingpoint stack, genericity permits us to define a generic class of type
stack and later instantiate it either as an integer stack, a character stack or a floating-
point stack as may be required. This can be achieved by assigning a suitable value to a
parameter used in the generic class definition.
Dynamic binding
In method invocation, static binding is said to occur if the address of the called
method is known at compile time. In dynamic binding, the address of an invoked
method is known only at the run time. We have already seen that dynamic binding is
useful for implementing a type of polymorphism. It is important to remember that
dynamic binding occurs when we have some methods of the base class overridden by
the derived classes and the instances of the derived classes are stored in instances of
the base class.
Related Technical Terms
Persistence
Objects usually get destroyed once a program finishes execution. Persistent
objects are stored permanently. That is, they live across different executions. An object
can be made persistent by maintaining copies of it in a secondary storage or in a
database.
Agents
A passive object is one that performs some action only when requested through
invocation of some of its methods. Agents (also called active objects) on the other hand
monitor events occurring in the application and take action autonomously. Agents are
used in applications such as monitoring exceptions. For example, in a database
application like accounting, agents may monitor the balance sheet and would alert the
user whenever inconsistencies arise in a balance sheet due to some improper
transaction take place.
Widget
The term widget stands for window object. A widget is a primitive object used in
the design of graphical user interface (GUI). More complex graphical user interface
design primitives (widgets) can be derived from the basic widget using the inheritance
mechanism. A widget maintains internal data such as the geometry of the window,
background and foreground colours of the window, cursor shape and size, etc. The
methods supported by a widget manipulate the stored data and carry out operations
such as resize window, iconify window, destroy window, etc. Widgets are becoming the
standard components of GUI design. This has given rise to the technique of
component-based user interface development.
4. Advantages of OOD
In the last few years that OOD has come into existence, it has found widespread
acceptance in the industry as well as in the academics. The main reason for the
popularity of OOD is that it holds the following promises:
 Code and design reuse
 Increased productivity
 Ease of testing and maintenance
 Better code and design understandability
Out of all these advantages, many people agree that the chief advantage of OOD
is improved productivity-which comes about due to a variety of factors, such as:
 Code reuse by the use of predefined class libraries
 Code reuse due to inheritance
 Simpler and more intuitive abstraction, i.e. better organization of inherent
complexity
 Better problem decomposition
Compiled results indicate that when companies start to develop software using
the object-oriented paradigm, the first few projects incur higher costs than the costs
incurred by the traditionally developed projects. After a few projects have been
completed, reductions in cost become possible. A functioning and well-established
object-oriented methodology and environment is likely to be managed with 20-50% of
the cost of traditional development. Having discussed the basic concepts of object-
orientation, we now present the Unified Modelling Language (UML).
M.Sc (IT) Part-II PAPER : MS(A)-213
UML-II
Unified Modelling Language (UML)

As the name implies, UML is a modelling language. It provides a set of notations
(e.g. rectangles, lines, ellipses, etc.) to create models of systems. Models are very useful
in documenting the design and analysis results. Models also facilitate the generation of
analysis and design procedures themselves.
However, we must be clear that UML is not a system design or development
methodology. Neither is it tied to any specific methodology. It can only be used to
document object-oriented analysis and design results obtained using some
methodology. In fact, it was one of the objectives of the developers of UML to keep the
notations of UML independent of any specific design methodology. In this respect, UML
is different from its predecessors (e.g. OMT, Booch's methodology, etc.) where the
notations supported by the modelling languages were closely tied to the corresponding
design methodologies.
Origin
In the late 1980s and the early 1990s, there was a proliferation of object-
oriented design techniques and notations. Different software development houses were
using different notations to document their object-oriented designs. It was also not
uncommon to find different project teams in the same organization using different
notations for documenting their object-oriented analysis and design results. These
diverse notations used to give rise to a lot of confusion.
UML was developed to standardize the large number of object-oriented
modelling notations that existed and were used extensively in the early 1990s. The
principal ones in use were:
 OMT [Rumbaugh 1991]
 Booch's methodology [Booch 1991]
 OOSE [Jacobson 1992]
 Odell's methodology [Odell 1992]
 Shlaer and Mellor methodology [Shlaer 1992]
Figure 1: Schematic representation of the impact of different object modelling

techniques on UML
Needless to say that UML has borrowed many concepts from these modelling
techniques. Especially, concepts from the first three methodologies have been heavily
drawn upon. The influence of various object modelling techniques on UML is
schematically represented in Figure 1. As shown in this figure, OMT has had the most
profound influence on UML.
UML was adopted by Object Management Group (OMG) as a de facto standard
in 1997. Actually, OMG is not a standard formulating body, but an association of
industries which tries to facilitate early formulation of standards. OMG aims to
promote consensus notations and techniques with the hope that if the usage becomes
widespread, then they would automatically become standards. For more information
on OMG, visit www.omg.org.
UML is more complex than its antecedents. This is expected because it is
intended to be more comprehensive. We shall see that UML contains an extensive set of
notations and suggest construction of many types of diagrams. UML has successfully
been used to model both large and small problems. The elegance of UML, its adoption
by OMG, and a strong industry backing have helped UML find widespread acceptance.
UML is now being used in a large number of software development projects world-wide.
It is interesting to note that the use of UML is not restricted to the software industry
alone. As an example of UML's use outside the software development problems, some
car manufacturers are planning to use UML for their "build-to-order" initiative.
Many of the UML notations are difficult to draw on paper and are best drawn
using a CASE tool such as Rational Rose (visit www.rational.com). Many of the
available CASE tools also help in incremental elaboration of the initial object model to
final design. Some CASE tools also generate code templates in a variety of languages
once the UML models have been constructed.
What is a model?
Before we discuss the details of UML, it is important to clearly understand what
exactly is meant by a model and why is it necessary to create a model. A model
captures aspects important for some applications while omitting (or abstracting) the
rest. A model in the context of software development can be graphical, textual,
mathematical or program code based. Graphical models are very popular because they
are easy to understand and construct. UML is primarily a graphical modelling tool.
However, text explanations are often required to accompany the graphical models.
Why construct a model?
An important reason behind constructing a model is that it helps manage
complexity. Once the models of a system have been constructed, these can be used for
a variety of purposes during software development, including the following:
 Analysis
 Specification
 Code generation
 Design
 Visualize and understand the problem and the working of a system
 Testing, etc.
In all these applications, the UML models can be used not only to document the
results but also to arrive at the results themselves. Since a model can be used for a
variety of purposes, it is reasonable to expect that the model would vary depending on
the purpose for which it is being constructed. For example, a model developed for
initial analysis and specification will be very different from the one used for design. A
model that is being used for analysis and specification would not show any of the
design decisions that would be made later on during the design stage. On the other
hand, a model used for design purposes should capture all the design decisions.
Therefore, it is a good idea to explicitly mention the purpose for which a model has
been developed.
UML Diagrams
UML can be used to construct nine different types of diagrams to capture
different views of a system. Just as a building can be modelled from several views (or
perspectives) such as ventilation perspective, electrical perspective, lighting
perspective, heating perspective, etc. the different UML diagrams provide different
perspectives of the software system to be developed and facilitate a comprehensive
understanding of the system. Such models can be refined to get the actual
implementation of the system.
The UML diagrams can capture the following views of a system:

 Structural view
 Behavioural view
 Implementation view
 Environmental view
We first provide a brief overview of the different views of a system which can be
developed using UML. The diagrams used to realize the important views are discussed
in the subsequent sections
Figure 2: Different types of diagrams and views supported in UML
Users' view: This view defines the functionalities (facilities) made available by the
system to its users. The users' view captures the external users' view of the system in
terms of the functionalities offered by the system. The users' view is a black-box view of
the system where the internal structure, the dynamic behavior of the different system
components, the implementation, etc. are not visible. The users' view is very different
from all other views in the sense that it is a functional model compared to the object
model of all other views.
The users' view can be considered as the central view and all other views are
expected to conform to this view. This thinking is in fact the crux of any user-centric
development style. It is indeed remarkable that even for object-oriented development,
we need a functional view.
Structural view: The structural view defines the kinds of objects (classes) important to
the understanding of the working of a system and to its implementation. It also

captures the relationships among the classes (objects). The structural model is also
called the static model, since the structure of a system does not change with time.
Behavioural view: The behavioural view captures how objects interact with each other
to realize the system behaviour. The system behaviour captures the time-dependent
(dynamic) behaviour of the system.
Implementation view: This view captures the important components of the system
and their dependencies.
Environmental view: This view models how the different components are implemented
on different pieces of hardware.
For any given problem, should one construct all the views using all the
diagrams provided by UML? The answer is "No". For a simple system, the use case
model, class diagram, one of the interaction diagrams may be sufficient. For a system
in which the objects undergo many state changes, a state chart diagram may be
necessary. For a system, which is implemented on a large number of hardware
components, a deployment diagram may be necessary. So, the type of models to be
constructed depends on the problem at hand. Rosenberg provides an analogy saying
that, "Just like you do not use all the words listed in the dictionary while writing a
prose, you do not use all the UML diagrams and modelling elements while modelling a
system."
Use Case Model
The use case model for any system consists of a set of "use cases". Intuitively, use
cases represent the different ways in which a system can be used by the users. A
simple way to find all the use cases of a system is to ask the question: "What the users
can do using the system?" Thus for the Library Information System (LIS), the use cases
could be:
 issue-book
 query-book
 return-book
 create-member
 add-book
The use cases partition the system behaviour into transactions, such that each
transaction performs some useful action from the user's point of view. Each
transaction may involve either a single message or multiple message exchanges
between the user and the system to complete itself.
The purpose of a use case is to define a piece of coherent behaviour without
revealing the internal structure of the system. The use cases do not mention any
specific algorithm to be used, nor the internal data representation, internal structure of
the software, etc. A use case typically represents a sequence of interactions between
the user and the system. These interactions consist of one main line sequence. The
mainline sequence represents the normal interaction between a user and the system.
The mainline sequence is the most frequently; occurring sequence of interaction. For
example, in the mainline sequence of the withdraw cash the use case supported by a
bank ATM would be: you insert the card, enter password, select the amount withdraw
option, enter the amount to be withdrawn, complete the transaction and get the
amount. Several variations to the main line sequence may also exist. Typically, a
variation from the mainline sequence occurs when some specific conditions hold. For
the bank ATM example, variations or alternate scenarios may occur, if the password is
invalid or the amount to be withdrawn exceeds the account balance. These variations
are also called alternate paths. A use case can be viewed as a set of related scenarios
tied together by a common goal. The mainline sequence and each of the variations are
called scenarios or instances of the use case. Each scenario is a single path of user
events and system activity through the use case.
The use case model is an important analysis and design artifact. As already
mentioned, other UML models must conform to this model in any use case-driven (also
called the user. centric) analysis and development approach. It should be remembered
that the "use case model" is not really an object-oriented model according to a strict
definition of the term., The use case model represents a functional or process model of
a system. This is in contrast to all other types of UML diagrams.
Normally, each use case is independent of the other use cases. However,
implicit: dependencies among use cases may exist because of dependencies among the
use cases at the implementation level resulting from shared resources, objects or
functions. For example, in our Library Automation System, renew-book and reserve-
book are two independent use cases. But in actual implementation of renew-book, a
check is made to see if any book has been reserved by a previous execution of the
reserve-book use case. Another example of dependency among use cases is the
following. In the Bookshop Automation Software" update-inventory and sale-book are
two independent use cases. But during execution of sale-book there is an implicit
dependency on update-inventory.
Representation of Use Cases
Use cases can be represented by drawing a use case diagram and writing an
accompanying text elaborating the drawing. In the use case diagram, each use case is
represented by an ellipse with the name of the use case written inside the ellipse. All
the ellipses (i.e. use cases) of a system are enclosed within a rectangle which
represents the system boundary. The name of the system being modelled (e.g. Library
Information System) appears inside the rectangle.
The different users of the system are represented by using the stick person icon.
Each stick person icon is normally referred to as an actor. An actor is a role played by
a User with respect to the system use. It is possible that the same user may play the
role of multiple actors. Each actor can participate in one or more use cases. The line
connecting the actor and the use case is called the communication relationship. It
indicates that the actor makes use of the functionality provided by the use case.
Both the human users and the external systems can be represented by stick
person icons. When a stick person icon represents an external system, it is annotated
by the stereotype «external system».
At this point, it is necessary to explain the concept of a stereotype in UML. One
of the main objectives of the creators of the UML was to restrict the number of
primitive symbols in the language. A language having a large number of primitive
symbols not only becomes difficult to learn but also makes it difficult to use these
symbols and understand a diagram constructed by using a large number of basic
symbols. The creators of UML introduced a stereotype which when used to annotate a
basic symbol can slightly change the meaning of the basic symbol. Just as you
stereotype your friends in the class as studious, jovial, serious, etc. stereotyping can be
used to give special meaning to any basic UML construct.
Example 1
The use case model for the Tic-tac-toe problem is shown in Figure 3. This
software has only one use case "play move". Note that we did not name the use case
"get-user-move". The name "get-user-move" would be inappropriate because the use
cases should be named from the users' perspective.
Figure 3: Use case model for Example 1
Text Description. Each ellipse on the use case diagram should be accompanied by a
text description. The text description should define the details of the interaction
between the user and the computer and other aspects of the use case. It should
include all the behaviour associated with the use case in terms of the mainline
sequence, different variations to the normal behaviour, the system responses
associated with the use case, the exceptional conditions that may occur in the
behaviour, etc. The behaviour description is often written in a conversational style
describing the interactions between the actor and the system. The text description may
be informal, but some structuring is recommended. The following are some of the types
of information which may be included in a use case text description in addition to the
mainline sequence and the alternate scenarios.
Contact persons. This section lists the personnel of the client organization with whom
the use case was discussed, date and time of the meeting, etc.
Actors. In addition to identifying the actors, some information about the actors using
this use case which may help in the implementation of the use case may be recorded.
Pre-condition. The pre-conditions would describe the state of the system before the
use case execution starts.
Post-condition. This captures the state of the system after the use case has been
successfully completed
Nonfunctional requirements. This could contain the important constraints for the
design and implementation, such as platform and environment conditions, qualitative
statements, response time requirements, etc.
Exceptions, error situations. This contains only the domain-related errors such as
lack of user's access rights, invalid entry in the input fields, etc. Obviously, errors
that are not domain related, such as software errors, need not be discussed here.
Sample dialogs. These serve as examples illustrating the use case.
Specific user interface requirements. These contain specific requirements for the
user interface of the use case. For example, it may contain forms to be used,
screenshots, interaction style etc.
Document references. This part contains references to specific domain-related
documents which may be useful to understand the system operation.
Example 2
The use case diagram of the Supermarket Prize Scheme is shown in Figure 4.
Figure 4: Use case model for Example 2

Text description
UI: register-customer: Using this use case, the customer can register himself by
providing the necessary details.
Scenario 1: Mainline sequence

1. Customer: select register customer option
2. System: display prompt to enter name, address and telephone number.
3. Customer: enter the necessary values
4. System: display the generated id and the message that the customer has been
successfully registered.
Scenario 2: At step 4 of the mainline sequence.
4. System: displays the message that the customer has already registered.
Scenario 3: At step 4 of the mainline sequence.
4. System: displays the message that some input information has not been
entered. The system displays a prompt to enter the missing values.
U2: register-sales: Using this use case, the clerk can register the details of the
purchase made by a customer.
1. Clerk: selects' the register sales option.
2. System: displays prompt to enter the purchase details and the id of the
customer
3. Clerk: enters the required details.
4. System: displays a message of having successfully registered the sale.
U2: select-winners: Using this use case, the manager can generate the winner list.
1. Manager: selects the select-winner option.
2. System: displays the gold coin and the surprise gift winner list.
7.4.2 Why Develop the Use Case Diagram?
If you look at the use case diagram, the utility of the use cases represented by
the ellipses is obvious. They along with the accompanying text description serve as a
type of requirements specification of the system and form the core model to which all
other models must conform. But what about the actors (stick person icons)? One
possible use of identifying the different types of users (actors) is in selecting and
implementing a security mechanism through a login system, so that each actor can
invoke only those functionalities to which he is entitled to. Another possible use is in
preparing the documentation (e.g. user's manual) targeted to each category of user.
Further, actors help in identifying the use cases and understanding the exact
functioning of the system.
How to Identify the Use Cases of a System?
Identification of the use cases involves brainstorming and reviewing the existing
requirements specification document. The actor-based method of identifying the use
cases is quite popular. This involves first identifying the different types of actors and
their usage in the system. For each actor, we identify the functions they initiate or
participate in.
Essential vs. Real Use Case

Essential use cases are created during the early stage of eliciting the
requirements. They are independent of the design decisions and tend to be correct over
long periods of time.
Real use cases describe the functionality of the system in terms of its actual
current design committed to specific input/output technologies. Therefore, the real use
cases can be developed only after the design decisions have been made. Real use cases
are a design artifact. However, sometimes organizations commit to development
contracts on the basis of, user interface specifications. In such cases, there is no
distinction between the essential use case and the real use case.
Factoring of Commonality Among Use Cases
It is often desirable to factor use cases into component use cases. Actually,
factoring of use cases is required under two situations. First, the complex use cases
need to be factored into simpler use cases. This would not only make the behaviour
associated with the use case, much more comprehensible, but also make the
corresponding interaction diagrams more tractable. Without decomposition, the
interaction diagrams for complex use cases may become too large to be accommodated
on a single standard-sized (A4) paper. Secondly, use cases need to be factored
whenever there is common behaviour across different use cases, Factoring would make
it possible to define such behaviour only once and reuse it wherever, required. It is
desirable to factor out common usage such as error handling from a set of use cases.
This makes analysis of the class design much simpler and elegant. However, a word of
caution here, Factoring of use cases should not be done except for achieving the above
two objectives. From the design point of view, it is not advantageous to break up a use
case into many smaller parts just for the sake of it. UML offers three mechanisms to
factor out the use cases which are discussed in the following:
Generalization
Use case generalization can be used when you have one use case that is similar
to another, but does something slightly differently or something more. Generalization
works the same way with use cases as it does with classes. The child use case inherits
the behaviour and meaning of the present use case. The notation is the same too (See
Figure 5). It is important to remember that the base and the derived use cases are
different from one another and therefore should have separate text descriptions.
Figure 5: Representation of Use case gener

n
Includes
The includes relationship in the older versions of UML (prior to UML 1.1) was
known as the uses relationship. The includes relationship involves one use case
including the behaviour of another use case in its sequence of events and actions. The
includes relationship occurs when you have a chunk of behaviour that is similar
across a number of use cases. The factoring of such behaviour will help in avoiding the
repetition of the specification and its implementation across different use cases. Thus,
the includes relationship explores the issue of reuse by factoring out the commonality
across use cases. It can also be gainfully employed to decompose a large and complex
use case into more manageable parts.
As shown in Figure 6, the includes relationship is represented using a predefined
stereotype «include». In the includes relationship, a base use case compulsorily and
automatically includes the behaviour of the common use case. As shown in Figure 7,
issue-book and renew-book both include check-reservation use case. The base use
case may include several use cases. In such cases, it may interleave their associated
common use cases together. The common use case becomes a separate use case and
independent text description should be provided for it.
Figure 6: Representation of Use case inclusion
Figure 7: Paralleling model
Extends
The main idea behind the extends relationship among use cases is that it allows
you to show optional system behaviour. An optional system behaviour is executed only
under certain conditions. This relationship among use cases is also predefined as a
stereotype as shown in Figure 8.
Figure 8: Example Use case extension

The extends relationship is similar to generalization. But unlike generalization,
the extend use case can add additional behaviour only at an extension point when
certain conditions are satisfied. The extension points are points within the use case
where variation to the mainline (normal) action sequence may occur. The extends
relationship is normally used to capture alternate paths or scenarios.
Organization of use cases

When the use cases are factored, they are organized hierarchically. The high-
level use cases are refined into a set of smaller and more refined use cases as shown in
Figure 9, The top-level use cases are superordinate to the refined use cases. The
refined use cases are subordinate to the top-level use cases. Note that only the complex
use cases should be decomposed and organized in a hierarchy. It is not necessary to
decompose simple use cases.
The functionality of the superordinate use cases is traceable to their
subordinate use cases. Thus, the functionality provided by the superordinate use cases
is composite of the functionality of the subordinate use cases.
In the highest level of the use case model, only the fundamental use cases are
shown. The focus is on the application context. Therefore, this level is also referred to
as the context diagram. In the context diagram, the system limits are emphasized. In
the top-level diagram, only those use cases with which external users interact are
shown. The topmost use cases specify the complete services offered by the system to
the external users of the system. The subsystem-level use cases specify the services
offered by the subsystems. Any number of levels involving the subsystems may be
utilized. In the lowest level of the use case hierarchy, the class-level use cases specify
the functional fragments or operations offered by the classes.
Use Case Packaging
Packaging is the mechanism provided by UML to handle complexity. When we
have too many use cases in the top-level diagram, we can package the related use
cases so that at best 6 or 7 packages are present at the top level diagram. Any
modelling element that becomes large and complex can be broken up into packages.
Please note that you can put any element of UML (including another package) in a
package diagram, The symbol for a package is a folder. Just as you organize a large
collection of documents in a folder, you can organize UML elements into packages. An
example of packaging use cases is shown in Figure 10.
Figure 9: Hierarchical organization of Use case
Class Diagrams
A class diagram describes the static structure of a system. It shows how a
system is structured rather than how it behaves. The static structure of a system
consists of a number of class diagrams and their dependencies. The main constituents
of a class diagram are classes and their relationships: generalization, aggregation,
association, and various kinds of dependencies. We now discuss the UML syntax for
representation of the classes and their relationships.
Figure 10: Use case packaging

Classes
The classes represent entities with common features, i.e. attributes and
operations. Classes are represented as solid outline rectangles with compartments.
Classes have a mandatory name compartment where the name is written centered in
boldface. The class name is usually written using mixed case convention and begins
with an uppercase. The class names are usually chosen to be singular nouns. An
example of various representations of a class is shown in Figure 11.
Figure 11: Different representations of Library Member class

Classes have optional attributes and operations compartments. A class may appear
on several diagrams. Its attributes and operations are suppressed on all but one
diagram.
Attributes
An attribute is a named property of a class. It represents the kind of data that
an object might contain. Attributes are listed with their names and may optionally
contain specification of their type (that is, their class, e.g. Int, Book, Employee, etc.), an
initial value, and constraints. Attribute names are written left-justified using plain type
letters, and the names should begin with a lowercase letter.
Attribute names may be followed by square brackets containing a multiplicity
expression, e.g. sensorStatus[l0]. The multiplicity expression indicates the number of
attributes per instance of the class. An attribute without square brackets must hold
exactly one value. The type of an attribute is written by appending a colon and the type
name after the attribute name, for example, sensorStatus[1]:lnt.
The attribute name may be followed by an equal sign and an initial value that is
used to initialize the attributes of the newly created objects, for example,
sensorStatus[1]:lnt=O.
Operation
The operation names are typically left-justified, in plain type and always begin
with a lowercase letter. (Remember that abstract operations are those for which the
implementation is not provided during the class definition.) The parameters of a
function may have a kind specified. The kind may be "in" indicating that the parameter
is passed into the operation, or "out" indicating that the parameter is only returned
from the operation, or "inout" indicating that the parameter is used for passing data
into the operation and getting result from the operation. The default is "in".
An operation may have a return type consisting of a single return type
expression, for example, issueBook(in bookName):Boolean. An operation may have a
class scope (i.e. shared among all the objects of the class) and is denoted by
underlining the operation name.
Often a distinction is made between the two terms, i.e. operation and method.
An operation is something that is supported by a class and invoked by objects of other
classes. Please remember that there might be multiple methods implementing the same
operation. We pointed out earlier that this is called static polymorphism. The method
names can be the same; however, it should be possible to distinguish the methods by
examining their parameters. Thus, the terms operation and method are distinguishable
only when there is polymorphism.
Association
Associations are needed to enable objects to communicate with each other. An
association describes a connection between classes. The relation between two objects
is called object connection or link. Links are instances of associations. A link is a
physical or conceptual connection between object instances. For example, suppose
Amit has borrowed the book Graph Theory. Here, borrowed is the connection between
the objects Amit and Graph Theory book. Mathematically, a link can be considered to
be a tuple, i.e. an ordered list of object instances. An association describes a group of

links with a common structure and common semantics. For example, consider the
statement that Library Member borrows Books. Here, borrows is the association
between the class LibraryMember and the class Book.
Usually, an association is a binary relation (between two classes). However,
three or more different classes can be involved in an association. A class can have an
association relationship with itself (called recursive association). In this case, it is
usually assumed that two different objects of the class are linked by the association
relationship.
Association between two classes is represented by drawing a straight
line between the concerned classes. Figure 12 illustrates the graphical representation
of the
Figure 12: Association between two classes
association relation. The name of the association is written along side the association
line. An arrowhead may be placed on the association line to indicate the reading
direction of the association. The arrowhead should not be misunderstood to be
indicating the direction of a pointer implementing an association. On each side of the
association relation, the multiplicity is noted as an individual number or as a value
range. The multiplicity indicates how many instances of one class are associated with
the other. Value ranges of multiplicity are noted by specifying the minimum and
maximum value, separated by two dots.
Associations are usually realized by assigning appropriate reference attributes
to the classes involved. Thus, associations can be implemented using pointers from
one object class to another. Links and associations can also be implemented by using a
separate class that stores which objects of a class are linked to which objects of
another class. Some CASE tools use the role names of the association relation for the
corresponding automatically generated attribute.
Aggregation
Aggregation is a special type of association where the involved classes represent a
whole part relationship. The aggregate takes the responsibility of forwarding messages
to the appropriate parts. Thus, the aggregate takes the responsibility of delegation and
leadership.
We have already seen that if an instance of one object contains instances of some
other objects, then aggregation (or composition) relationship exists between the
composite object and the component object. Aggregation is represented by the diamond
symbol at the composite end of a relationship. The number of instances of the
component class aggregated can also be shown as in Figure 13.
Figure 13: Representation of Aggregation
The aggregation relationship cannot be reflexive (i.e. recursive). That is, an

object cannot contain objects of its own class. Also, the aggregation relation is not
symmetric. That is, two classes A and B cannot contain instances of each other.
However, the aggregation relationship can be transitive. In this case, aggregation may
consist of an arbitrary number of levels
Composition
Composition is a stricter form of aggregation, in which the parts are existence-
dependent on the whole. This means that the life of each part is closely tied to the life
of the whole. When the whole is created, the parts are created and when the whole is
destroyed, the parts are destroyed.
A typical example of composition is an invoice object with invoice items. As soon
as the invoice object is created, all the invoice items in it are created and as soon as
the invoice object is destroyed, all invoice items in it are also destroyed. The
composition relationship is represented as a filled diamond drawn at the composite-
end. An example of the composition relationship is shown in Figure 14.
Figure 14: Representation of Composition

Inheritance
The inheritance relationship is represented by means of an empty arrow pointing from
the subclass to the superclass. The arrow may be directly drawn from the subclass to
the superclass. Alternatively, the inheritance arrow from the subclasses may be
combined into a single line (see Figure 15).
Figure 15: Representation of inheritance relationship

The direct arrows allow flexibility in laying out the diagram and can easily be
drawn by hand. The combined arrows emphasize the collectivity of the subclass, and
that the specialization has been done on the basis of some discriminator. In the
example of Figure 15, issuable and reference are the discriminators. The various
subclasses of a superclass can then be differentiated by means of the discriminator.
The set of subclasses of a class having the same discriminator is called a partition. It is
often helpful to mention the discriminators during modelling, as these become
documented design decisions.
Dependency
Dependency is a form of association between two classes. A dependency relation
between two classes shows that a change in the independent class requires a change to
be made to the dependent class. Dependencies are shown as dotted arrows
(see Figure 16).
Figure 16: Representation of dependence between classes
Dependencies may have various causes. Two important reasons for dependency among
classes are the following:
 A class invokes the methods provided by another class.
 A class uses a specific interface of another class. If the properties of the class
that provides the interface are changed, then a change becomes necessary in
the class that uses that interface.
Constraints
Constraints describe a condition or an integrity rule. It can describe the
permissible set of values of an attribute, specify the pre- and post-conditions for
operations, define a specific order, etc. For example, {Constraint} UML allows you to
use any free form expression to describe constraints. The only rule is that they are to
be enclosed within braces. Constraints can be expressed using informal English.
However, UML also provides Object Constraint Language (OCL) to specify constraints.
Object diagrams
Object diagrams, show the snapshots of the objects in a system at a point in
time. Since it shows instances of classes, rather than the classes themselves, it is often
called the instance diagram. The objects are drawn using rounded rectangles (see
Figure 17).
An object diagram may undergo continuous change as the execution proceeds.
For example, links may get formed between objects or get broken. Objects may get
created and destroyed, and so on. Object diagrams are useful in explaining the working
of the system.
Interaction Diagrams
Interaction diagrams are models that describe how groups of objects collaborate
to realize some behaviour. Typically, each interaction diagram realizes the behaviour of
a single use case. An interaction diagram shows a number of example objects and the
messages that are passed between the objects within the use case.
There are two kinds of interaction diagrams: sequence diagrams and
collaboration diagrams. These two diagrams are equivalent in the sense that anyone
diagram can be derived automatically from the other. However, they are both useful.
These two actually portray the different perspectives of behaviour of the system and
different types of inferences can be drawn from them.
Figure 17: Different representation of a LibraryMember object

Sequence diagram
A sequence diagram shows interaction among objects as a two-dimensional
chart. The chart is read from top to bottom. The objects participating in the interaction
are shown at the top of the chart as boxes attached to a vertical dashed line. Inside the
box the name of the object is written with a colon separating it from the name of the
class, and both the name of the object and the classes are underlined.
The objects appearing at the top signify that the objects already existed when
the use case execution was initiated. However, if some object is created during the
execution of the use case and participates in the interaction (e.g. a method call), then
that object should be shown at the appropriate place on the diagram where it was
created.
The vertical dashed line is called the object's lifeline. The lifeline indicates the
existence of the object at any particular point of time. The rectangle drawn on the
lifeline is called the activation symbol and indicates that the object is active as long as
the rectangle exists. Each message is indicated as an arrow between the lifelines of two
objects. The messages are shown in chronological order from the top to the bottom.
That is, reading the diagram from the top to the bottom would show the sequence in
which the messages occur. Each message is labelled with the message name. Some
control information can also be included. Two types of control information are
particularly valuable.
 A condition (e.g. [invalid]) indicates that a message is sent, only if the condition
is true.
 An iteration marker (*) shows that the message is sent many times to multiple
receiver objects as would happen when you are iterating over a collection or the
elements of an array. You can also indicate the basis of the iteration, for
example, [for every book object].
The sequence diagram for the book renewal use case for the library automation
software is shown in Figure 18.
Figure 18: Sequence diagrams for the renew book use case
Figure 18 : Sequence diagram for the renew boot use case
Collaboration diagram
A collaboration diagram shows both the structural and behavioural aspects
explicitly. This is unlike a sequence diagram which shows only the behavioural
aspects. The structural aspect of a collaboration diagram consists of objects and the
links existing between them. In this diagram, an object is also called a collaborator.
The behavioral aspect is described by the set of messages exchanged among the
different collaborators. The link between objects is shown as a solid line and can be
used to send messages between two objects. The message is shown as a labelled arrow
placed near the link.
Messages are prefixed with sequence numbers because that is the only way to
describe the relative sequencing of the messages in this diagram.
The collaboration diagram for the example of Figure 18 is shown in Figure 19. A
use of the collaboration diagrams in our development process would be to help us
determine which classes are associated with which other classes.
Figure 19: Collaboration diagrams for the renew book use case
Activity Diagrams
The activity diagram is possibly one modelling element which was not present in
any of the predecessors of UML. No such diagrams were present either in the works of
Booch, Jacobson or Rumbaugh. It is possibly based on the event diagram of Odell
[1992] though the notation is very different from that used by Odell.
The activity diagram focuses on representing activities or chunks of processing
which may or may not correspond to the methods of classes. An activity is a state with
an internal action and one or more outgoing transitions which automatically follow the
termination of the internal activity. If an activity has more than one outgoing
transitions, then these must be identified through conditions.
Activity diagrams are similar to the procedural flow charts. The difference is that
activity diagrams support description of parallel activities and synchronization aspects
involved in different activities.
An interesting feature of the activity diagrams is the swim lanes. Swim lanes
enable you to group activities based on who is performing them, for example, academic
department vs. hostel office. Thus swim lanes subdivide activities based on the
responsibilities of some components. The activities in swim lanes can be assigned to
some model elements, for example, classes or some component, etc.
Figure 20: Activity diagrams for student admission procedure at IIT
Activity diagrams are normally employed in business process modelling. This is

carried out during the initial stages of requirements analysis and specification. Activity
diagrams can be very useful to understand complex processing activities involving
many components. Later these diagrams can be used to develop interaction diagrams
which help to allocate activities (responsibilities) to classes.
The student admission process in IIT is shown as an activity diagram in Figure 20.
This diagram shows the part played by different components of the Institute in the
admission procedure. After the fees are received at the account section, parallel
activities start at the hostel office, hospital and the department. After all these activities
complete (this synchronization is represented as a horizontal line), the identity card
can be issued to a student by the Academic section.
State Chart Diagram
A state chart diagram is normally used to model how the state of an object
changes in its lifetime. State chart diagrams are good at describing how the behaviour
of an object changes across several use case executions. However, if we are interested
in modelling some behaviour that involves several objects collaborating with each
other, the state chart diagram is not appropriate.
State chart diagrams are based on the finite state machine (FSM) formalism. An
FSM consists of a finite number of states corresponding to those of the object being
modelled. The object undergoes state changes when specific events occur. The FSM
formalism existed long before the object-oriented technology and has since been used
for a wide variety of applications. Apart from modelling, it has even been used in
theoretical computer science as a generator for regular languages.
A major disadvantage of the FSM formalism is the state explosion problem. The
number of states becomes too many and the model too complex when used to model
practical systems. .This problem is overcome in UML by using state charts. The state
chart formalism was proposed by David Harel [1990]. A state chart is a hierarchical
model of a system and introduces the concept of a composite state (also called the
nested state). Actions are associated with transitions and are considered to be
processes that occur quickly and are not interruptible. Activities are associated with
states and can take longer. An activity can be interrupted by an event.
The basic elements of the state chart diagram are as follows:
Initial state. It is represented as a filled circle.
Final state. It is represented by a filled circle inside a larger circle.
State. It is represented by a rectangle with rounded corners.
Transition. A transition is shown as an arrow between two states. Normally, the name
of the event which causes the transition is placed along side the arrow. You can also
assign a guard to the transition. A guard is a Boolean logic condition. The transition
can take place only if the grade evaluates to true. The syntax for the label of the
transition is shown in three parts: event[guard]/action.
An example state chart for the order object of the Trade House Automation
software is shown in Figure 21.
Summary
 In this lesson we have reviewed some important concepts associated with the
object-oriented methodology.
 One of the primary advantages of the object-oriented approach is increased
productivity of the software development team. The reason why object-oriented
projects achieve dramatically higher levels of productivity can be attributed
primarily to reuse of predefined classes and partly to reuse due to inheritance,
and the conceptual simplicity brought about by the object approach.
 Object modelling plays an important role in analyzing, designing, and under.
standing systems. UML has rapidly gained popularity and is poised to become a
standard in object modelling.
 UML can be used to construct five different views of a system using nine
different kinds of diagrams. However, it is not mandatory to construct all views
of a system using all types of diagrams in a modelling effort. The types of
models to be constructed depend on the problem at hand.
 We discussed the syntax and semantics of some important types of diagrams
which can be constructed using UML.
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Sc (IT) PART-II PAPER: MS(A)-213

LESSON No. 1 AUTHOR: VISHAL SINGH

Software Engineering: Introduction

programmer writes a complete program, while a software engineer writes a software

e. Uniformity : The notation and use of comments, specific keywords and

5.0 A History of Software Engineering

procedures. No doubt, it will continue to be important to learn how to build better

10.0 Self check exercise

LESSON No. 2 AUTHOR: VISHAL SINGH

The Software Life Cycle

1.0 The Software Life Cycle

the following phases.

2.2 Product and Process Qualities

specifications." We will follow this convention when the context is clear.

In general, the user friendliness of a system depends on the consistency of its

such as reliability or performance is pointless.

LESSON No. 3 AUTHOR: VISHAL SINGH

Software Process Models

The stages of "The Waterfall Model" are:

preparation of incomplete specifications or the conversion of limited prototypes into

Best projects to use Prototyping

physical to logical grouping should be flexible enough to reflect changes in

LESSON No. 4 AUTHOR: VISHAL SINGH

Project Planning and Organization

 Organizing. Organizing involves the establishment of clear lines of authority

computing resources (e.g. workstations, personal computers, and data-base

experience has shown that programmers developing application programs produce as

Size Attributes Source Instruction

Programme Attributes Type

Computer attributes Time Constraint

Personal attributes Personnel capability

Project attributes Tools and techniques

Table : 2 Factors used in cost estimation models

2.3.1 Predictive models of software cost

Table 5: Efforts multipliers used by COCOMO intermediate model

Figure 1: Work breakdown structure for a complier project

The Gantt chart in the figure is actually an enhanced version of standard

3.3 PERT Charts

Some of the advantages of PERT are as follows:

corporation or to orchestrate the interactions among programmers who report to a

Another issue affecting the appropriate project organization is the nature of

consultants, as well as what tasks they perform, is determined by the chief

programming" because it encourages programmers to share and review one another's

4.3 Mixed control team organization

A mixed-mode organization tries to limit communication to within a small

LESSON No. 5 AUTHOR: VISHAL SINGH

The Requirement Analysis

Considering the multiple stakeholders involved, the list of requirements gathered in

7. What should the SRS address?

4.1.1 Description and priority

Table 2 shows a more detailed SRS outline

1. Scope 1.1 Identification.

2. Referenced 2.1 Project documents.

3. Requirements This section shall be divided into paragraphs to specify the

6. Notes This section contains information of a general or

Identify and Link Requirements with Sources

ID Paragraph Requirement Business Rule Source