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Knowledge Management in Software

Engineering: A Systematic Review of
Studied Concepts, Findings and Research
Methods Used1
Finn Olav Bjørnson1 and Torgeir Dingsøyr1,2
1
Norwegian University of Science and Technology Department of Computer and
Information Science, Sem Sælandsvei 7-9, 7491 Trondheim, Norway, Tel.:+ 47 73 59
87 16, fax: + 47 73 59 44 66, e-mail: bjornson@idi.ntnu.no
2
SINTEF Information and Communication Technology, SP Andersens vei 15b, 7465
Trondheim, Norway, Tel.: +47 73 59 29 79, fax: +47 73 59 29 77, e-mail:
torgeir.dingsoyr@sintef.no

Abstract. Software engineering is knowledge-intensive work, and how to manage

software engineering knowledge has received much attention. This systematic review
identifies empirical studies of knowledge management initiatives in software
engineering, and discusses the concepts studied, the major findings, and the research
methods used. Seven hundred and sixty-two articles were identified, of which 68 were
studies in an industry context. Of these, 29 were empirical studies and 39 reports of
lessons learned. More than half of the empirical studies were case studies.
The majority of empirical studies relate to technocratic and behavioural aspects of
knowledge management, while there are few studies relating to economic, spatial and
cartographic approaches. A finding reported across multiple papers was the need to
not focus exclusively on explicit knowledge, but also consider tacit knowledge. We
also describe implications for research and for practice.

Keywords: software engineering, knowledge management, learning software

organization, software process improvement, systematic review

1 Postprint of: Bjørnson, F. O., & Dingsøyr, T. (2008). Knowledge Management in Software Engineering: A Systematic Review
of Studied Concepts and Research Methods Used. Information and Software Technology, 50(11), 1055-1168.
doi:10.1016/j.infsof.2008.03.006
https://www.sciencedirect.com/science/article/abs/pii/S0950584908000487
Released under Creative Commons Attribution Non-Commercial No Derivatives License.

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1. Introduction
Software engineering is a knowledge-intensive activity. For software organisations,
the main assets are not manufacturing plants, buildings, and machines, but the
knowledge held by the employees. Software engineering has long recognized the need
for managing knowledge and the community could learn much from the knowledge-
management community, which bases its theories on well-established disciplines such
as cognitive science, ergonomics, and management.

As the field of software engineering matures, there is an increased demand for

empirically-validated results and not just the testing of technology, which seems to
have dominated the field so far. A recent trend in software engineering is an increased
focus on evidence-based software engineering, EBSE [43, 67]. Since the volume of
research in the field is expanding constantly, it is becoming more and more difficult to
evaluate critically and to synthesise the material in any given area. This has lead to an
increased interest in systematic reviews (SR) [66] within the field of software
engineering.

In this article, we report on a systematic review of empirical studies of knowledge

management in software engineering. Our goal is to provide an overview of empirical
studies within this field, what kinds of concepts have been explored, what the main
findings are, and what research methods are used. More specifically we ask the
following research questions:

1. What are the major knowledge management concepts that have been
investigated in software engineering?
2. What are the major findings on knowledge management in software
engineering?
3. What research methods have been used within the area so far?

Our target readership is three groups that we think will be interested in an overview of
empirical research on knowledge management in software engineering: (1)
researchers from software engineering who would like to design studies to address
important research gaps, and identify relevant research methods; (2) researchers on
knowledge management in general, who would be interested in comparing work in

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the software engineering field to other knowledge-intensive fields; and (3) reflective
practitioners in software engineering, who will be interested in knowing what
knowledge management initiatives have been made in software companies, or quickly
identifying relevant studies, and the major findings and implications from these.

The remainder of this article is structured as follows. Section 2 presents the

background and general theories on knowledge management. Section 3 describes the
research method that we used to select and review the data material for our research,
and presents our chosen framework for analysis. Section 4 presents the results of the
systematic review according to our chosen framework. In Section 5, we discuss the
findings and their implications. For research, we identify what we belive are the most
important research gaps. For practitioners, we provide advice on how to use the
results in practice. Section 6 concludes.

2. Background
In this chapter, we first give a brief background on knowledge management, then give
an overview of theories often referred to in the knowledge management literature.
Finally, we give an overview of existing work on knowledge management in software
engineering.

2.1 Knowledge management

Knowledge management is a large interdisciplinary field. There is, as a consequence,
an ongoing debate as to what constitutes knowledge management. However, it is
beyond the scope of this article to engage in that debate. For our purposes, it is
sufficient to cite some definitions that are in common use. Davenport has defined
knowledge management as ”a method that simplifies the process of sharing,
distributing, creating, capturing and understanding of a company’s knowledge” [28].
A related term is organisational learning. What does it mean to say that an
organisation as a whole learns? According to Stata, this differs from individual
learning in two respects [112]: first, it occurs through shared insight, knowledge and
shared models; second, it is based not only on the memory of the participants in the
organisation, but also on “institutional mechanisms” such as policies, strategies,
explicit models and defined processes (we can call this the “culture” of the
organisation). These mechanisms may change over time, what we can say is a form of
learning.

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Knowledge management has received much attention in various fields, which is

demonstrated by the publication of two “handbooks” [33, 45], one encyclopaedia
[106], and numerous books [25, 28, 109].

Hanssen et al. [55] refer to two main strategies for knowledge management:
• Codification – to systematise and store information that constitutes the
knowledge of the company, and to make this available to the people in the
company.
• Personalisation – to support the flow of information in a company by having a
centralised store of information about knowledge sources, like a ”yellow
pages” of who knows what in a company.

Earl [44] has further classified work in knowledge management into schools (see
Table 1). The schools are broadly categorized as “technocratic”, “economic” and
“behavioural”. The technocratic schools are 1) the systems school, which focuses on
technology for knowledge sharing, using knowledge repositories; 2) the cartographic
school, which focuses on knowledge maps and creating knowledge directories; and 3)
the engineering school, which focuses on processes and knowledge flows in
organizations.

The economic school focuses on how knowledge assets relates to income in

organizations.

The behavioural school consists of three subschools: 1) the organizational school,

which focuses on networks for sharing knowledge; 2) the spatial school, which
focuses on how office space can be designed to promote knowledge sharing; and 3)
the strategic school, which focuses on how knowledge can be seen as the essence of a
company’s strategy.

Table 1: Earl’s schools of knowledge management.

Technocratic Economic Behavioural
Systems Cartographic Engineering Commercial Organizational Spatial Strategic
Focus Technology Maps Processes Income Networks Space Mindset
Aim Knowledge Knowledge Knowledge Knowledge Knowledge Knowledge Knowledge
bases directories flows assets pooling exchange capabilities
Unit Domain Enterprise Activity Know-how Communities Place Business

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There are a number of overview articles of the knowledge management field in the
literature. In the following we describe overview articles from management science
and information systems.

In the introduction to the book Challenges and Issues in Knowledge Management

[22], in the field of management consulting, Buono and Poulfelt claim that the field is
moving from first to second generation knowledge management. In first generation
knowledge management, knowledge was considered a possession, something that
could be captured, thus knowledge management was largely a technical issue on how
to capture and spread the knowledge through tools like management information
systems, data repositories and mechanistic support structures. The second generation
of knowledge management is characterized by knowing-in-action. Knowledge is
though of as a socially embedded phenomenon, and solutions have to consider
complex human systems, communities of practice, knowledge zones, and organic
support structures. The change in knowledge management initiatives is seen to go
from a planned change approach to a more guided changing approach.

Coming from the field of management consulting, Christensen [26] performed a

literature review focusing on special journal issues on knowledge management from
1995-2003. He performed a content analysis of 50 identified papers focusing on
knowledge management context, knowledge management outcomes, empirical setting
and the key drivers for knowledge management. The finding was that KM writings
seem to focus on how to create knowledge and to a lesser degree, how to transfer
knowledge. The categories that did not receive adequate coverage were integration,
production, measurement, retention and reflection. A second finding was that the
drivers for both knowledge creation and knowledge transfer were generic and to a
large degree overlapping. He goes on to explore knowledge management in practice
through 10 managers from industry and compares his results to the results of the
theoretic study. The main conclusion is that KM theory does reflect, in generic terms,
the practices that support KM activities, but the challenge is to observe this practical
application of generic drivers, which often is difficult to observe in practice.

In the information systems field, Alavi and Leidner [3] summarize literature from
different fields, which is relevant to research on knowledge management systems.
One of the major challenges in KM according to them is to facilitate the flow of

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knowledge between individuals so that the maximum amount of transfer occurs. They
also conclude that no single or optimal solution to organizational knowledge
management can be developed. Instead a variety of approaches and systems needs to
be employed to deal with the diversity of knowledge types. Knowledge management
is not a monolithic but a dynamic and continuous phenomenon.

Liao gives an overview of technology and applications for knowledge management in

a review of the literature from 1995 to 2002 [74]. The review covers knowledge-based
systems, data mining, ICT applications, exptert systems, database technology and
modelling technology.

Argote et al. [7] conclude a special issue of Management Science with an article that
provides a framework for organizing the literature on knowledge management,
identifies emerging themes, and suggests directions for further research.

Many have been critical to the concept of knowledge management, and in particular
to the use of information technology in knowledge management. Hislop [56]
questions the distinction between tacit and explicit knowledge. If explicit knowledge
cannot be managed independently, this means that information technology will have a
smaller part in knowledge management. This critique is also supported by McDermot,
[85] who argues that “if people working in a group don’t already share knowledge,
don’t already have plenty of contact, don’t already understand what insights and
information will be useful to each other, information technology is not likely to create
it”. In addition, Swan et al. [114] criticize the knowledge management field for being
too occupied with tools and techniques. They claim that researchers tend to overstate
the codifiability of knowledge and to overemphasize the utility of IT to give
organizational performance improvement. They also warn that “codification of tacit
knowledge into formal systems may generate its own pathology: the informal and
locally situated practices that allow the firm to cope with uncertainty may become
rigidified by the system”.

Schultze and Leidner [105] studied discourses of knowledge management in

information systems research, and warn that knowledge can be a double-edged sword:
too little can result in expensive mistakes, while too much can lead to unwanted
accountability. In a study of research on information systems, they found that most

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existing research is optimistic on the role of knowledge management in organizations,

and they urge researchers to give more attention to the critique of knowledge
management.

2.2 Theories of organizational learning

In cognitive and organization science, we find many models on how knowledge is
transferred or learned at an individual and organizational level. We present four
theories that are referred to widely: Kolb’s model of experiential learning, the double-
loop learning theory of Argyris and Schön, Wenger’s theory of communities of
practice, and Nonaka and Takeuchi´s theory of knowledge creation.

Kolb describes learning from experience (“experiential learning”, see [70]) as four
different learning modes that we can place in two dimensions. One dimension is how
people take hold of experience, with two modes, either relying on symbolic
representation – which he calls comprehension, or through “tangible, felt qualities of
immediate experience”, which he calls apprehension. The other dimension is how
people transform experience, with two modes, either through internal reflection,
which he refers to as intention, or through “active external manipulation of the
external world”, which he calls extension.

Kolb argues that people need to take advantage of all four modes of learning to be
effective, they “must be able to involve themselves fully, openly, and without bias in
new experiences; reflect on and observe these experiences from many perspectives;
create concepts that integrate their observations into logically sound theories; and use
these theories to make decisions and solve problems” [71].

Argyris and Schön distinguish between what they call single and double-loop learning
[9] in organisations. In single-loop learning, one receives feedback in the form of
observed effects and then acts on the basis solely of these observations to change and
improve the process or causal chain of events that generated them. In double-loop
learning, one not only observes the effects of a process or causal chain of events, but
also understands the factors that influence the effects [8].

One traditional view of learning is that it is most effective when it takes place in a
setting where you isolate and abstract knowledge and then “teach” it to “students” in

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rooms free of context. Wenger describes this as a view of learning as an individual

process where, for example, collaboration is considered a kind of cheating [120]. In
his book about communities of practice, he describes a completely different view:
learning as a social phenomenon. A community of practice develops its own
“practices, routines, rituals, artefacts, symbols, conventions, stories and histories”.
This is often different from what you find in work instructions, manuals and the like.
Wenger defines learning in communities of practice as follows:

For individuals: learning takes place in the course of engaging in, and contributing to,
a community.
For communities: learning is to refine the practice.
For organisations: learning is to sustain interconnected communities of practice.

Nonaka and Takeuchi [90] claim that knowledge is constantly converted from tacit to
explicit and back again as it passes through an organisation. By tacit knowledge [94]
we mean knowledge that a human is not able to express explicitly, but is guiding the
behaviour of the human. Explicit knowledge is knowledge that we can represent in
textual or symbolic form. They say that knowledge can be converted from tacit to
tacit, from tacit to explicit, or from explicit to either tacit or explicit knowledge. These
modes of conversion are described as follows:

Socialization means to transfer tacit knowledge to another person through

observation, imitation and practice, what has been referred to as “on the job” training.
Externalisation means to go from tacit knowledge to explicit. Explicit knowledge can
“take the shapes of metaphors, analogies, concepts, hypotheses or models”.
Internalisation means to take externalised knowledge and make it into individual tacit
knowledge in the form of mental models or technical know-how.
Combination means to go from explicit to explicit knowledge, by taking knowledge
from different sources such as documents, meetings, telephone conferences, or
bulletin boards and aggregating and systematizing it.

According to Nonaka and Takeuchi, knowledge passes through different modes of

conversion, which makes the knowledge more refined and spreads it across different
layers in an organisation.

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2.3 Knowledge management in software engineering

Companies developing information systems have failed to learn effective means for
problem solving to such an extent that they have learned to fail, according to an article
by Lyytinen and Robey [81]. One suggested mean to overcome this problem is an
increased focus on knowledge management.

There are many approaches to how software should be developed, which also affect
how knowledge is managed. A main difference between methods here is if they are
plan-based or traditional, which rely primarily on managing explicit knowledge, or
agile methods, which primarily rely on managing tacit knowledge [88].

In software engineering, there has been much discussion about how to manage
knowledge, or foster “learning software organisations”. In this context, Feldmann and
Althoff have defined a “learning software organisation” as an organisation that has to
“create a culture that promotes continuous learning and fosters the exchange of
experience” [50]. Dybå places more emphasis on action in his definition: “A software
organisation that promotes improved actions through better knowledge and
understanding” [41].

In software engineering, reusing life cycle experience, processes and products for
software development is often referred to as having an “Experience Factory” [13]. In
this framework, experience is collected from software development projects, and are
packaged and stored in an experience base. By packing, we mean generalising,
tailoring, and formalising experience so that it is easy to reuse.

In 1999, the first workshop on “learning software organizations” was organized in

conjunction with the SEKE conference. This workshop has been one of the main
arenas for empirical studies as well as technological development related to
knowledge management in software engineering.

The May 2002 issue of IEEE Software [77] was devoted to knowledge management
in software engineering, giving several examples of knowledge management
applications in software companies. In 2003, the book “Managing Software
Engineering Knowledge” [40] was published, focusing on a range of topics, from
identifying why knowledge management is important in software engineering [78], to

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supporting structures for knowledge management applications in software

engineering, to offering practical guidelines for managing knowledge.

However, Edwards notes in an overview chapter in the book on Managing Software

Engineering Knowledge [47] that knowledge management in software engineering is
somewhat distanced from mainstream knowledge management.

Several PhD thesis have also been published on aspects of knowledge management
that are related to software engineering [16, 18, 36, 117].

In addition, a number of overviews of work on knowledge management in software

engineering have previously been published. Rus et al. [100] present an overview of
knowledge management in software engineering. The review focuses on motivations
for knowledge management, approaches to knowledge management, and factors that
are important when implementing knowledge management strategies in software
companies. Lindvall et al. [80] describe types of software tools that are relevant for
knowledge management, including tools for managing documents and content, tools
for managing competence, and tools for collaboration. Dingsøyr and Conradi [37]
surveyed the literature for studies of knowledge management initiatives in software
engineering. They found eight reports on lessons learned, which are formulated with
respect to what actions companies took, what the effects of the actions were, what
benefits are reported, and what kinds of strategy for managing knowledge were used.

Despite of the previously published overviews of the field, there is still a lack of broad
overviews of knowledge management in software engineering. Our motivation for
this study was thus, to give a more thorough and broader overview in the form of a
systematic review. This study also covers recent work, and assesses the quality of the
research in the field.

3. Method
The research method used is a systematic review [66], with demands placed on
research questions, identification of research, selection process, appraisal, synthesis,
and inferences. We now address each of these in turn.

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3.1 Planning the review

We started by developing a protocol for the systematic review, specifying in advance
the process and methods that we would apply. The protocol specified the research
questions, the search strategy, criteria for inclusion and exclusion, and method of
synthesis.

The aim of the study was to provide an overview of the empirically studied methods
for knowledge management in software engineering, answering the research questions
listed in Section 1.

3.2 Identification of research

A comprehensive, unbiased search is a fundamental factor that distinguishes a
systematic review from a traditional review of the literature. Our systematic search
started with the identification of keywords and search terms. We used general
keywords in the search in order to identify as many relevant papers as possible.

Table 2: Keywords for our search

Software engineering keywords Knowledge management keywords
• software engineering • knowledge management
• software process • tacit knowledge
• learning software organization • explicit knowledge
• knowledge creation
• knowledge acquisition
• knowledge sharing
• knowledge retention
• knowledge valuation
• knowledge use
• knowledge application
• knowledge discovery
• knowledge integration
• knowledge Theory
• organization knowledge
• knowledge engineering
• experience transfer
• technology transfer

All possible permutations of the software engineering and knowledge management

concepts were tried in the search conducted. The following electronic bases were
those we considered most relevant [42]: ISI Web of Science, Compendex, IEEE
Xplore and the ACM Digital Library.

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In addition, we identified two arenas that, to our knowledge, are the only ones that
pertain specifically to knowledge management in software engineering: the workshop
series on Learning Software Organisations (LSO) from 1999 until 2006, and the book
Managing Software Engineering Knowledge [10]. We searched all proceedings from
the workshop series and included all chapters from the book.

We performed the search in August 2006, which means that publications up to and
including the first quarter of 2006 are included, but some studies in the second quarter
might not have been indexed in the databases.

The identification process yielded 2102 articles. This formed the basis for the next
step in our selection process.

3.3 Selection of primary studies

The first step after the articles had been identified was to eliminate duplicate titles,
and titles clearly not related to the review. One researcher (the first author) read
through the 2102 titles and removed duplicates and those clearly not related to the
field of software engineering. This yielded a result of 762 articles.

After this we obtained the abstract of these articles and both authors read through all
abstracts, with the following exclusion criterion.
• Exclude if the focus of the paper is clearly not on software engineering
• Exclude if the focus of the paper is clearly not on knowledge management
• Exclude if the method, tool or theory described is not tested in industry

To narrow the search further we also decided to focus on technical and process
knowledge (thus, “software engineering knowledge”). Hence, we also used the
criterion
• Exclude if the focus of the paper is on domain knowledge

After each researcher had gone through the papers we compared results. Where we
disagreed as to whether to keep or remove a paper, we discussed the matter until we
reached agreement.

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This process reduced the number of articles to 133, and agreement between
researchers was ‘good’ (Kappa value of 0,655).

The full text for all 133 papers was obtained and both researchers read through all the
papers with the same criteria for exclusion in mind. The final number of papers
selected for the review was 68. The agreement between researchers at this stage was
“moderate” (Kappa value: 0,523).

3.4 Quality assessment and classification

We chose to classify the 68 papers identified along two axes. (1) We wanted to
examine what kinds of concept had been tested. To aid us with this we chose the
framework for classifying strategies for managing knowledge presented by Earl in
[44]. Each researcher classified the 68 papers individually according to the
framework, before comparing the results. Disagreements were discussed until a
consensus was reached on the classification. (2) We also wanted to examine the
scientific rigour of the studies. Here we settled on a simpler classification. All studies
included so far had results taken from industry. We further assessed the quality of the
selected papers by categorising these into empirical studies and lessons learned
reports. The criterion for being accepted as an empirical study and not a report of
lessons learned was that the article had a section describing the research method and
context. Again, each study was classified individually by the two researchers before
comparing the results and discussing problem cases in order to reach agreement. After
the quality assessment, we had 29 empirical studies and 39 reports of lessons learned.

3.5 Synthesis
For the synthesis, we chose to only use the papers classified as empirical studies in
our framework, in order to avoid problems associated with lessons learned reports
stemming from their lack of scientific rigor. We extracted concepts covered, main
findings and the research method for each article. One researcher (the first author)
focused on the studies in the technocratic schools, while the other researcher (the
second author) focused on the behavioural schools.

4. Results
Using the framework outlined in Section 3.4, we categorized the 29 empirical studies
and 39 reports of lessons learned in Table 3. For a complete listing of papers in each

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category, see the appendix. Within Earl’s framework, we found a heavy concentration
on the technocratic schools and a fair mention of the behavioural school. We did not
find any papers relating to the economic school with our search criterion. Within the
technocratic schools, systems and engineering stand out as areas that have received
much attention. Within the behavioural schools, organizational and strategic have
received the most attention.

Four of the empirical studies did not fit into Earl’s framework. These were classified
as studies on the impact of knowledge management initiatives and on knowledge
management per se. Thus, we ended up with 25 studies classified as empirical within
the framework. Of the 39 reports of lessons learned, two belonged to two categories,
which is why we ended up with a sum of 41 for the reports of lessons learned in the
table.
Table 3: Categorized articles

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Empirical studies 6 1 12 0 3 0 3 25
% distribution, empirical studies 24 4 48 0 12 0 12 100
Lessons learned reports 20 0 9 0 2 1 9 41
% distribution, lessons learned reports 49 0 22 0 5 2 22 100

Looking at the papers by year of publication, presented in Figure 1, we notice an

increasing interest in the area from 1999 onwards. We also notice a shift from more
papers on lessons learned to empirical papers from 2003 onwards. The apparent
decrease in attention in 2006 is due to our covering only the first third of this year,
since our search was conducted in August.

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16

14

12

10
Lessons Learned
8
Empirical studies
6

0
92
93
94
95
96
97
98
99
00
01
02
03
04
05
06
19
19
19
19
19
19
19
19
20
20
20
20
20
20
20
Figure 1: Publications by year

To obtain an overview of the research methods used within this field, we used the
classification presented in Glass et al. [52]. This was carried out on the 25 papers
classified as empirical studies. The result is presented in Table 4. See the appendix for
a complete listing of which paper was classified in which category.

Table 4: Overview of research methods

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Systems 1 3 1 1 6
Cartographic 1 1
Engineering 1 8 1 2 12
Organizational 3 3
Strategic 1 2 3
Sum 3 14 2 1 5 25
% 12 56 8 4 20 100

In the following subsections, we present the concepts and main findings from the
empirical studies within the main knowledge management schools.

4.1 Technocratic schools

The technocratic schools are based on information or management technologies,
which largely support and, to different degrees, condition employees in their everyday
tasks. We identified a total of 19 empirical studies and 29 papers on lessons learned in
this category. The main focus is on the engineering and systems schools.

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4.1.1 Systems
As defined by Earl, the systems school is built on the underlying principle that
knowledge should be codified in knowledge bases. This is what Hansen et al. refer to
as the “codification strategy”, and what Nonaka and Takeuchi refer to as
externalization.

This school is the longest established school of knowledge management, and it is in

this category we found the oldest papers in our search. Most of the papers that were
excluded would have been placed in this category, if they had contained empirical
results from industry. They could mainly be classified as conceptual analysis and
concept implementation, according to Glass’s definition. In total, we classified six
papers as empirical in this school, and 20 as lessons learned. The empirical papers in
this category can broadly be defined as either dealing with the development or use of
knowledge repositories. In what follows, we briefly present the major concepts
studied in the empirical papers. An overview of concepts and findings can be found in
Table 5.

Table 5: Concepts and main findings for the systems school

School Concepts Main Findings Reference
Systems Development of Approach to supporting risk in [11]
knowledge repositories and project management
initial use Users should be involved in [20]
development
Approach to support design [24, 111]
activities
Use of knowledge Benefits can be realized [72]
repositories over time quickly, tool remains useful
over time, and more benefits
accrue over time
Tool can be used for different [35]
kinds of knowledge than
originally intended

In [24], Chewar and McCrickard present their conclusions from three case studies
investigating the use of their knowledge repository. On the basis of their case studies,
they present general guidelines and tradeoffs for developing a knowledge repository.
In [20], Bjørnson and Stålhane follow a small consulting company that wanted to
introduce an experience repository. On the basis of interviews with the employees,
they draw conclusions about attitudes towards the new experience repository, and the
content and functionality preferred by the employees. Barros et al. [11] investigate

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how risk archetypes and scenario models can be used to codify reusable knowledge
about project management. They test their approach by an observational analysis in
industry. They also describe a feasibility study within an academic environment.

Concerning the actual usage of experience repositories or knowledge bases, Dingsøyr

and Røyrvik [35] investigate the practices in a medium-sized software consulting
company where knowledge repositories are used in concrete work situations. They
found several distinct ways of using the tool and highlight the importance of informal
organization and the social integration of the tool in daily work practices. A more
formal approach to knowledge management tools is found in [111], where Skuce
describes experiences from applying a knowledge management tool in the design of a
large commercial software system. Concerning long-term effects of experience
repositories, Kurniawati and Jeffrey [72] followed the usage of a combined electronic
process guide and experience repository in a small-to-medium-sized software
development company for 21 weeks, starting a year after the tool was introduced.
They conclude that tangible benefits can be realized quickly and that the tool remains
useful with more benefits accruing over time.

4.1.2 Cartographic
The principal idea of the cartographic school is to make sure that knowledgeable
people in an organization are accessible to each other for advice, consultation, or
knowledge exchange. This is often achieved through knowledge directories, or so-
called ”yellow pages”, that can be searched for information as required.

Table 6: Concepts and main findings for the cartographic school

School Concepts Main Findings Reference
Cartographic Use of cartographic Tool was used for: allocating [17]
system resources, searching for
competence, identifying project
oportunities and upgrading skills.
Tool enabled learning practice at [17]
both individual and company
level.

We found only one empirical paper within this school and no papers on lessons
learned. In [34], Dingsøyr et al. examine a skills management tool at a medium-sized
consulting company. They identify four major usages of the tool and point out

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implications of their findings for future or other existing tools in this category, see
Table 6.

4.1.3 Engineering
The engineering school of knowledge management is a derivative or outgrowth of
business process reengineering. Consequently it focuses on processes. According to
our classification, the largest amount of empirical papers came from this school. Two
major categories can be identified. The first contains work done by researchers who
investigate the entire software process with respect to knowledge management. The
second contains work done by researchers who focus more on specific activities and
how the process can be improved within this activity. Table 7 gives an overview of
concepts and findings for this school.

Table 7: Concepts and main findings for the engineering school

School Concepts Main Findings Reference
Engineering Managing It is feasible to use knowledge [14]
knowledge on the management as underlying theory to
software develop key process areas to
development supplement the CMM
process No matter what knowledge [6]
management approach you pursue in
SPI, you need to create both tacit
and explicit knowledge. Tacit is
neccesary to change practice,
explicit is neccesary to create an
organizational memory.
A techno-centric approach to SPI [108]
may impose unnatural work
practices on an organisation and
fails to take account of how process
improvements might occur
spontaneously within a community
of practice.
The iterative approach of Unified [51]
Process ensures large effects in
terms of learning, but Unified
Process also improves on
communication and work
distribution in the company.
It is possible to define and [118]
implement software process in a
beneficial and cost-efficient manner
in small software organisations.
Special considerations must be given
to their specific business goals,
models, characteristics, and resource

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limitations.
Managing Formal routines must be [27]
knowledge supplemented by collaborative,
through formal social processes to promote effective
routines dissemination and organizational
learning.
Mapping of Knowledge mapping can [54]
knowledge flows successfully help an organisation to
select relevant focus areas for
planning future improvement
initiatives.
Casual maps for risk modeling [2]
contributes to organizational
learning
Process for Creating a suitable environment for [32]
conducting reflection, dialogue, criticism, and
project reviews to interaction is salient to the
extract conducting of a postmortem.
knowledge The organizational level can only [101]
benefit from the learning of project
teams if the knowledge and
reasoning behind the process
improvements is converted into such
an explicit format that it can be
utilized for learning in
organizational level also.
Implications of The focus on the pure codified [86]
social interaction approach is the critical reason of
on knowledge Tayloristic team failure to
sharing effectively share knowledge among
all stakeholders of a software
project.
Increasing the level of reflection in [19]
mentor programs can result in more
double looped learning.

Baskerville and Pries-Heje [15] used knowledge management as the underlying

theory to develop a set of key process areas to supplement the Capability Maturity
Model (CMM) [93] in a Small and Medium sized Enterprise (SME) software
development company. Realising that the CMM did not fit well with an SME
company, they helped their case companies to develop new key process areas that
focused on managing their knowledge capability. Arent et al. [6] address the
challenge of creating organizational knowledge during software process
improvement. They argue for the importance of creating organizational knowledge in
Software Process Improvement (SPI) efforts and claim that its creation is a major
factor for success. On the basis of an examination of several cases, they claim that

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both explicit and tacit knowledge are required, no matter what approach is pursued.
Segal [108] investigates organizational learning in software process improvement.
Using a case to initiate and implement a manual of best practice as a basis, she
observed that the ideal and actual scenarios of use differed and identified possible
reasons for the difference. In [51] Folkestad et al. studied the effect of using the
rational unified process as a tool for organizational change. In this case, it was used to
introduce development staff to a new technology and methodology. Folkestad et al.
concluded that the iterative approach of the unified process had obvious effects on
organisational and individual learning. The unified process also resulted in new
patterns of communication and a new division of labour being instituted, which had a
significant effect on the company. Wangenheim et al. [118] report on their
experiences of defining and implementing software processes. They confirm what
others have experienced, that it is possible to define and implement software
processes in the context of small companies in a beneficial and cost-effective way.

In the papers that focused on specific activities within the process, we identified four
major areas: formal routines, mapping of knowledge flows, project reviews, and
social interaction. Many of these processes are aimed at stimulating several ways of
learning, as, for example, Kolb suggests.

In [27] Conradi and Dybå report on a survey that investigated the utility of formal
routines for transferring knowledge and experience. Their main observation was that
developers were rather sceptical about using written routines, while quality and
technical managers took this for granted. Given this conflict of attitudes, they describe
three implications for research on this topic.

Hansen and Kautz [54] argue that if software companies are to survive, it is critical
that they improve continuously the services that they provide. Such improvement
depends, to a great extent, on the organization’s capability to share knowledge and
thus on the way knowledge flows in an organization. To investigate knowledge flow,
they introduced a tool to map the flows of organisational knowledge in a software
development company. Using their new method, they identify potential threats to
knowledge flows in an organisation. Also using flow diagrams, Al-Shehab et al. [2]
describe how learning from analyses of past projects and from the issues that
contributed to their failure is becoming a major stage in the risk management process.

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They introduce causal mapping as a method to visualise cause and effect in risk
networks. They claim that their method is useful for organisational learning, because
it helps people to visualise differences in perceptions.

In [32], Desouza et al. describe two ways of conducting project postmortems. They
stress that learning through postmortems must occur at three levels: individual, team,
and organization. The paper describes guidelines for when to select different kinds of
postmortem, depending on the context and the knowledge that is to be shared. The
authors also argue that postmortems must be woven into the fabric of current project
management practices. Salo [101] also studies postmortem techniques and concludes
that existing techniques lack a systematic approach to validating iteratively the
implementation and effectiveness of action taken to improve software processes. Salo
studies the implementation of a method to remedy this and observes that the
organisational level can only benefit from the learning of project teams if the
knowledge and reasoning behind the improvements to processes are converted into an
explicit format such that it can be utilized for learning at the organisational level.

In [86], Melnik and Maurer discuss the role of conversation and social interaction
effective knowledge sharing in an agile process. Their main finding suggests that the
focus on pure codification is the principal reason that Tailoristic teams fail to share
knowledge effectively. Moving the focus from codification to socialisation, Bjørnson
and Dingsøyr [17] investigated knowledge sharing through a mentor programme in a
small software consultancy company. They describe how mentor programmes could
be changed to improve the learning in the organization. They also identify several
unofficial learning schemes that could be improved.

4.2 Behavioural schools

The behavioural aspects of knowledge management are covered in three schools in
Earl’s framework: the organizational, spatial, and strategic schools. In our review, we
found three empirical studies and two reports of lessons learned in the organizational
school, no empirical study and one report of lessons learned in the spatial school, and
three empirical studies and nine reports of lessons learned in the strategic school. We
present the main concepts and findings from the organizational and strategic schools.

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4.2.1 Organizational
The organizational school focuses on describing the use of organizational structures
(networks) to share or pool knowledge. These structures are often referred to as
“knowledge communities”. Work on knowledge communities is related to work on
communities of practice as described in Section 2.2. An overview of our findings
from this school is presented in Table 8.

Table 8: Concepts and main findings for the organizational school

School Concepts Main Findings Reference
Organizational How networks are Networks should be used in [84]
used in software addition to other activities
engineering when introducing new
software engineering methods
Description of the role of [53]
networks.
Networks built on existing [91]
informal networks are more
likely to be successful

The role of networking as an approach to knowledge management has been

investigated in three settings where software is developed. Grabher and Ibert [53]
discuss what types of network exist in companies, where one case is a software
company based in Germany. Mathiassen and Vogelsang [84] discuss how to
implement software methods in practice and use two concepts from knowledge
management: networks and networking. The network perspective emphasizes the use
of technology for sharing knowledge, while networking focuses on trust and
collaboration among practitioners involved in software development. The authors
stress that knowledge management is highly relevant to understand challenges when
introducing new methods for software engineering, and that every company have to
find a suitable balance between strategies. In the case company, the emphasis on
networks and networking changed considerably during the project. Nörbjerg et al.
[91] discuss the advantages and limitations of knowledge networks. They base their
discussion on an analysis of two networks related to software process improvement in
a medium-sized software company in Europe. Their main finding is that building a
network on existing informal networks gave the highest value to the organization.

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4.2.2 Strategic
In the strategic school, knowledge management is seen as a dimension of competitive
strategy. Skandia’s views are a prime example [113]. Developing conceptual models
of the purpose and nature of intellectual capital has been a central issue. An overview
of our findings from this school is presented in Table 9.

Table 9: Concepts and main findings for the strategic school

School Concepts Main Findings Reference
Strategic What factors Suggested model, including [49]
contribute to technological, organizational
successful knowledge and human resource-factors
management
What learning Ongoing interaction between [5]
processes are used in different learning processes
practice important to improve practice
What strategies exist Found evidence of strategies [116]
for managing for codification and
software engineering personalization in software
knowledge companies

One important issue in the literature on knowledge management has been to identify
the factors that lead to the successful management of knowledge. Feher and Gabor
[49] developed a model of the factors that support knowledge management. The
model includes technological, organizational and human resource factors, and was
developed on the basis of data on 72 software development organizations that are
contained in the European database for the improvement of software processes.

Another issue of strategic importance is the processes that are in place to facilitate
learning. Arent and Nørjeberg [5] analysed three industrial projects for the
improvement of software processes, in order to identify the learning processes used.
They found that both tacit and explicit knowledge were important for improving
practice, and that improvement requires ongoing interaction between different
learning processes.

Trittmann [116] distinguish between two types of strategy for managing knowledge:
“mechanistic” and ”organic”. Organic knowledge management pertains to activities
that seek to foster innovation, while mechanistic knowledge management aims at

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using existing knowledge. A survey of 28 software companies in Germany supported

the existence of two such strategies. This work parallels the works of Hansen et al. on
codification and personalization as important strategies for managing knowledge in
the field of management science.

4.3 Knowledge management in general

Some studies could not be classified using Earl’s framework. These studies can be
placed in a broad category that encompasses works that seek to identify the impact of
knowledge management initiatives (two empirical studies), and works that investigate
knowledge management per se (two empirical studies). An overview of these are
presented in Table 10.
Table 10: Concepts and main findings for studies of knowledge management in general.
School Concepts Main Findings Reference
Knowledge The impact of Knowledge pull leads to more [1]
management knowledge effective knowledge
in general management initiatives management than knowledge
push
Knowledge needs to be [97]
internalized to improve
processes
Factors that enable Leadership is the most [119]
effective knowledge important enabler for
management knowledge management
Factors that contribute Perceived complexity, [31]
to use of knowledge perceived advantage and
artefacts perceived risk contribute to the
use of knowledge management
artefacts

4.3.1 The impact of knowledge management initiatives

Ajila and Sun [1] investigated two approaches to delivering knowledge to software
development projects: ”push” and ”pull”. “Push” means using tools to identify and
provide knowledge to potential users. “Pull” means that users themselves have to use
repositories and other tools to identify relevant knowledge. On the basis of a survey of
41 software companies in North America, the authors claim that pulling leads to more
effective software development.

Ravichandran and Rai [97] studied two models for how the embedding and creation
of knowledge influence software process capability. Embedding refers to the process
of employing knowledge in standard practices, for example through making work
routines, methods and procedures. They found support for a model where knowledge

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creation has an effect on process capability when the knowledge is embedded after it
is created. This means that knowledge has to be internalized before it can be used to
improve processes. The study was done as a survey of 103 Fortune 1000 companies
and federal and state government agencies in the US.

4.3.2 Knowledge management per se

Ward and Aurum [119] describe current practices for managing knowledge in two
Australian software companies and explain how leadership, technology, culture, and
measurements enable knowledge to be managed effectively and efficiently. They
found leadership to be the most significant positive factor for the management of
knowledge, but that the tools, techniques, and methodologies that the companies were
using were not adequate for managing knowledge effectively.

Desouza et al. [31] examined what factors contribute to the use of knowledge artefacts
in a survey of 175 employees in a software engineering organization. They
specifically looked at factors that govern the use of explicit knowledge. They found
that the following factors relate to the use of explicit knowledge: perceived
complexity, perceived relative advantage, and perceived risk.

5. Discussion
In this study, we have identified far more studies, particularly empirical studies, than
have been reported in previous assessments by Rus et al. [100], Lindvall [80] and
Dingsøyr and Conradi [37]. We have shown that although there are not many
empirical studies, except for in the systems and engineering schools, there are either
empirical studies or reports of lessons learned in all schools except the economic
school. Thus, research on knowledge management in software engineering seems to
be slowly gaining a broader focus, although research on knowledge management in
software engineering is still somewhat distanced from mainstream research on
knowledge management.

If we compare the studies found in software engineering to the research directions

suggested by Alavi et al. [3], we see that software engineering has primarily
addressed the storage and retrieval of knowledge, while topics such as knowledge
creation, the transfer and application of knowledge still needs more attention.

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We now discuss our findings. We begin with a discussion concerning our first two
research questions, then the third, outline implications for research and practice, and
end with a discussion of the validity of our study

5.1 Major knowledge management concepts and findings

To answer our two first research questions, we organize the discussion according to
Earl’s framework, answering “what are the major knowledge management concepts
that have been investigated in software engineering?“ and “what are the major
findings on knowledge management in software engineering?”

In this discussion of what we found, we will also include a discussion of how relevant
we think these knowledge management schools are for software engineering.
Software engineering is a large field with several disciplines relevant to knowledge
management, for example software process improvement. One recent development in
software engineering, which has implications for knowledge management activities is
whether a company seeks to have agile development processes in place, or rely on
tradition development methods such as the waterfall process [88]. Agile software
development will focus mainly on knowledge management activities related to tacit
knowledge, while the traditional development processes will need activities related to
explicit knowledge. In the following, we will discuss the concepts identified in
research, and give our opinion on what we think are the most relevant research areas
to support agile and traditional software development.

The final selection of papers was divided between the technocratic and behavioural
schools, with an emphasis on the technocratic side. This was not surprising, given the
general focus of software engineering on the construction of tools and processes. We
did not find any examples of what Earl considers economic schools. The reason for
this can be twofold, either few software companies track their intellectual capital, or
there is little interest in reporting findings from such activities in software
engineering.

5.1.1 Technocratic schools

The technocratic schools applied in software engineering can be interesting for other
knowledge-intensive disciplines as software engineers are likely to easily adopt new
information technology. Looking closer at these schools, we saw a heavy focus on the

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systems and engineering schools, with barely any mention of the cartographic school.
The heavy focus on the systems school can be explained by the software engineering
field’s focus on implementing new tools [37]. For this school, there is a greater
number of lessons learned reports than empirical studies. The main concepts we
identified in this school were the development and use of knowledge repositories.
There was, however, little to no overlap between the identified papers.

As for findings in this school, there are two studies of the use of knowledge
repositories over time, which shows that such tools are actually in use, and have more
benefits than the obvious. In Section 2.1, we referred to critique of the codification
strategy, and especially a belief that knowledge repositories easily can generate
information junkyards. There is not any evidence to support such a claim in software
engineering, but we believe there is a heavy publication bias towards success stories.
But the cases described in this review shows that it is possible to successfully
implement knowledge repositories to work in software companies.

The engineering school is the school that received the most empirical attention,
according to our review. Again, we identified two main areas within this school: those
focusing on the entire software process and those focusing on particular activities
within the process. Within the papers focusing on specific activities, we identified
four main areas: formal routines, mapping of knowledge flows, project reviews, and
social interaction. As with the systems school, there is little or no overlap between the
empirical studies. A possible explanation for the heavy empirical focus within this
school is the close fit with work on the improvement of software development
processes.
For the findings on whole development process, we see that having an established
development process can both improve communication and learning, but we also see
that it is important to focus also on sharing tacit knowledge in order to change
practice.

In relation to development processes for software, the systems and engineering

schools support sharing of explicit knowledge, which is important in traditional
software development. Both of these schools require a technical infrastructure in order
to facilitate knowledge sharing. However, a finding both from studies in other fields
of the systems school [62] and studies of a specific engineering activities, electronic

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process guides, is that it is difficult to get such technology in actual use [39].
However, many companies have invested in such infrastructure, and this indicates that
we need a better understanding of the factors that lead to effective knowledge sharing
within these two schools.

That there are so few papers in the cartographic school is interesting. One possible
explanation is that the ”yellow pages” systems are considered ”simple” and
undeserving of attention. Earl refers to a number of consulting companies using this
school, including McKinsey and Bain (see [55]). However, as the lone study in
software engineering shows, such tools have uses other than the obvious, and can
stimulate learning both at individual and organizational level. One argument for this
school is that although it requires a technical infrastructure, the investment is low
because there is no need to codify knowledge. This is a school which is relevant for
agile software development, and because of the growing number of such development
practices as well as the low cost, we think this is a school which requires further
research. A counter-argument could be that tacit knowledge is not as relevant for
software development as explicit knowledge, but we see from research on agile
development that it is possible to develop high-quality software without making much
use of explicit knowledge management [110].

5.1.2 Behavioural schools

In the behavioural schools, we found a limited number of papers focusing on
organizational and strategic aspects, and no papers focusing on spatial aspects.

The three studies in the organizational school discuss the use of people networks in
software organizations. Two of the studies investigated the improvement of software
development processes. In Earl’s taxonomy, both intra- and interorganizational
communities are mentioned as examples. In the software engineering literature, we
only find studies made in single organizations. Also, a much debated topic in general
knowledge management is what actions management can take in order to support this
type of knowledge sharing, what some refer to as knowledge governance. How much
should be formal, and what should be left to employees to organize themselves?

As for relevance for software engineering, we believe that this school has the potential
to deliver inexpensive solutions for companies, although as the studies in software

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engineering indicate, there is a debate on whether such initiatives are best left to grow
by themselves or if the management should have an active involvement. For software
engineering, it could be useful with studies that address this strategy in relation to
specific challenges for software development, like challenges with new technology,
process improvement or understanding customer needs. This school is relevant for
organizations that run multidisciplinary projects, which we believe is the case for
most software companies, whether they do agile or traditional development.

As for the spatial school, no empirical studies on software engineering were found in
this category. The question is then: Is this something that could be relevant in a
software engineering setting? The role of open-plan offices has been studied in other
fields, and this is something that also should have an impact on how knowledge is
shared in software teams. Many of the agile development methods recommend open-
plan offices, and knowing more about what specific effects this has on software
development would be valuable.

The empirical studies in the strategic school focus on factors pertaining to successful
knowledge management, learning processes, and types of strategy for managing
knowledge. It was, perhaps, to be expected that there would not be many articles
discussing the strategic importance of knowledge in software engineering supported
by empirical findings, because its importance is assumed in most published works on
knowledge management in software engineering.

5.2 Research methods

Our third research question addressed research methods used: “What research
methods have been used within the area so far?”

Of the 68 studies identified, 39 were reports of lessons learned and 29 were empirical
studies. Case studies constituted the largest number of empirical studies (see Table 4),
followed by field studies and action research. It is positive that the emphasis on
empirical studies has increased (see Figure 1). The apparent dip in 2006 is due to the
time at which the search was conducted. We searched the databases in August and
most compilers of databases take some months to index their papers; hence, we can
only claim to have covered the first third of 2006 fully.

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The research methods in the studies that we selected are dominated by case studies,
both single and multiple. This is not surprising, considering our limitation on only
including studies that performed tests in industry. We found one experiment, and it is
not surprising that there are few experiments. Knowledge management is a broad
field, and it is difficult to isolate factors for experiments without making the
experiment irrelevant.

An important question is then: Is it the right mixture of research methods that are
applied to study knowledge management in software engineering? Given the broad
nature of knowledge management, we believe it is right to have a large number of
case studies. But as the field matures, and we would like to see more studies of the
effects of knowledge management, we think we need more in-depth studies in
companies, which call for more studies oriented towards ethnography.

Glass et al. [52] found that empirical studies constitute about 5% of published
research in software engineering as a whole. Comparing our final findings to the
results from our first rough sorting of papers, our final selection constituted about 3%
of the initially selected papers. If we assume that Glass’s data are representative for
the area that we studied within software engineering, we could extrapolate that about
70% of those papers would be conceptual analysis and concept implementation. Most
of the papers discarded were indeed conceptual analysis and concept implementation
without empirical testing, our results do however, not show a discard number on the
empirical criterion as high as 70%. Many studies were also excluded because they
were not relevant to either software engineering or knowledge management.
Therefore it seems that empirical studies constitute a larger part of the studies on
knowledge management in software engineering than in software engineering in
general.

5.3 Implications for research and practice

This systematic review has implications both for researchers planning new studies of
knowledge management initiatives in software companies, and for practitioners
working in software companies who would like to design knowledge management
initiatives to meet local needs.

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5.3.1 Implications for research

For research, we think it is important to have in mind that what kind of knowledge
management activities a company should engage in should be determined by how the
company develops software. We have distinguished between two types of
development which has implications for strategy for knowledge management, namely
traditional and agile development.

In this systematic review, we have seen that the knowledge management schools
associated with traditional software development so far has received the most
attention, namely the systems and engineering schools. This is in line with the
observations of Buono and Poulfelt [22], indicating that knowledge management in
software engineering is mainly focusing on first generation knowledge management
in Section 2.1.

We believe the schools that are relevant to agile software development should be
given further attention in the future, as this trend seems to have much influence on
industry practice today. Another issue in deciding on priorities for research is the cost
of implementing activities in the schools. In general, the schools which do not require
codification and a technical infrastructure will be less expensive than the others.
Therefore, we argue that in particular the organizational school should be further
researched as this school is both relevant for agile and traditional software
development, and is inexpensive. Also, the cartographic and spatial schools are good
candidates for further research. As for research methods applied, we think there
should be a larger focus on in-depth studies, shown through a larger use of
ethnographic methods.

5.3.2 Implications for practice

As we indicated in implications for research, the technocratic schools are closely
related to traditional software development while the behavioural schools are more
related to the agile approach to development. The main consideration for practitioners
is thus that organisations developing software through a traditional approach will
probably benefit more from the technocratic schools, while agile teams would benefit
more from behavioural schools.

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Practitioners following a traditional approach can find some empirical papers and
several lessons learned reports on how to build a knowledge repository. Even though
all papers we identified within the systems school are positive it is important to
remember the objections to following a pure codification strategy we mentioned in
chapter 2.1. We believe there is potential bias in the number of positive reports from
this school versus those who report negative results. Our findings from the
engineering school also support this view, where several papers underline the
importance of not focusing exclusively on codification. An advantage of following the
technocratic approach to knowledge management is that there is more material
available within this “classical” school. A disadvantage is the cost of implementing
strategies relying heavily on codification.

The most important finding from the behavioural schools with implications for
practitioners developing in an agile environment would be that network building is
more likely to be successful if they are built on already existing networks. Also, the
need for diversity in both learning processes and strategies are stressed as important in
order to improve practice. An advantage of the behavioural approach to knowledge
management is the reduced cost compared to implementing the more application
heavy solutions in the technocratic school. However, it has its disadvantage in the
relatively few publications on this theme to learn from.

5.4 Limitations
The main threats to validity in this systematic review are threefold: our selection of
the studies to be included, correct classification of studies according to Earl’s
framework of schools in knowledge management, and potential author bias.

As for the selection of studies, only one researcher read through and discarded the
first results on the basis of the papers’ titles. However, in cases where there was
doubt, the papers were included in the next stage. The second and third selection
stages, which were based on abstracts and full papers, were carried out by both
researchers and we observed a ‘good’ degree of consensus. In cases where there was
disagreement, the issue was discussed until consensus was reached.

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Concerning the classification of studies, both researchers classified all papers

individualy before comparing the results. As before, in cases where there was
disagreement, the issue was discussed until consensus was reached.

Finally, there is a potential bias in that both authors have written papers that were
included in the review. Where only one author had participated in the primary study,
the other author decided whether or not to include it.

6. Conclusion
This systematic review has addressed the following research questions. 1) What are
the major knowledge management concepts that have been investigated in software
engineering? 2) What are the major findings on knowledge management in software
engineering? 3) What research methods have been used within the area so far?

For the first research question, our main findings are:

• The majority of studies of knowledge management in software engineering
relate to technocratic and behavioural aspects of knowledge management.
• The studies that report on concepts within the fields of technocratic and
behavioural aspects have very little overlap.
• There are few studies relating to economic, spatial and cartographic
approaches to knowledge management.

For the second research question, we found that:

• As for the concepts, the findings are also divided and have very little overlap.
• The major finding, which is repeated over several papers and across several
schools is the need to not focus exclusively on explicit knowledge but also on
tacit knowledge.

For the third research question, we found that:

• The majority of reports of applications of knowledge management in the
software engineering industry are reports of lessons learned, not scientific
studies.
• Of the reports categorized as empirical studies, more than half of the reports
are case studies.

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• Our search returned field studies, action research, ethnographic studies, and
one laboratory experiment.

The main implication for research is to focus more on the organizational school, while
we believe practitioners should focus also on activities to manage tacit knowledge
when working on knowledge management initiatives.

Acknowledgement
We are grateful to Reidar Conradi at the Department of Computer and Information
Science, Norwegian University of Science and Technology, and Tore Dybå at
SINTEF ICT for comments on an earlier version of this article. We would also like to
thank Chris Wright for proofreading and useful comments. This work was partially
funded by the Research Council of Norway through the project Evidence-Based
Software Engineering (181685/I30).

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Appendix
Table 11: Categorized articles, extended
Systems Cartographi Engineering Economic Organizational Spatial Strategic
c
Emp [11, 20, [34] [2, 6, 15, 17, [53, 84, 91] [5, 49,
24, 35, 27, 32, 51, 116]
72, 111] 54, 86, 101,
108, 118]
LL [4, 12, [4, 38, 48, [60, 61] [30] [21, 40,
23, 29, 64, 65, 83, 46, 63,
57-59, 98, 107, 75, 92,
68, 69, 115] 95, 103,
73, 76, 121]
79, 82,
87, 89,
96, 99,
102,
104,
107]

Table 12: Overview of research methods, extended

Research Method KM/SE
Action Research [5, 17, 20]
Case study [2, 6, 15, 24, 32,
51, 53, 72, 84, 91,
101, 108, 111,
118]
Ethnography [34, 35]
Laboratory Experiment [86]
Field Study [11, 27, 49, 54,
116]

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