research-article

Property-Based Software Engineering Measurement

Authors:

Lionel C. Briand,

Sandro Morasca,

Victor R. BasiliAuthors Info & Claims

IEEE Transactions on Software Engineering, Volume 22, Issue 1

Pages 68 - 86

https://doi.org/10.1109/32.481535

Published: 01 January 1996 Publication History

Abstract

Little theory exists in the field of software system measurement. Concepts such as complexity, coupling, cohesion or even size are very often subject to interpretation and appear to have inconsistent definitions in the literature. As a consequence, there is little guidance provided to the analyst attempting to define proper measures for specific problems. Many controversies in the literature are simply misunderstandings and stem from the fact that some people talk about different measurement concepts under the same label (complexity is the most common case).There is a need to define unambiguously the most important measurement concepts used in the measurement of software products. One way of doing so is to define precisely what mathematical properties characterize these concepts, regardless of the specific software artifacts to which these concepts are applied. Such a mathematical framework could generate a consensus in the software engineering community and provide a means for better communication among researchers, better guidelines for analysts, and better evaluation methods for commercial static analyzers for practitioners.In this paper, we propose a mathematical framework which is generic, because it is not specific to any particular software artifact, and rigorous, because it is based on precise mathematical concepts. We use this framework to propose definitions of several important measurement concepts (size, length, complexity, cohesion, coupling). It does not intend to be complete or fully objective; other frameworks could have been proposed and different choices could have been made. However, we believe that the formalisms and properties we introduce are convenient and intuitive. This framework contributes constructively to a firmer theoretical ground of software measurement.

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Yılmaz RBuzluca F(2024)A fuzzy logic-based quality model for identifying microservices with low maintainabilityJournal of Systems and Software10.1016/j.jss.2024.112143216:COnline publication date: 1-Oct-2024
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Index Terms

Property-Based Software Engineering Measurement
1. General and reference
  1. Cross-computing tools and techniques
    1. Metrics

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A view of software measurement that disagrees with the model presented in a recent paper by Kitchenham, Pfleeger, and Fenton, is given. Whereas Kitchenham, Pfleeger, and Fenton argue that properties used to define measures should not constrain the scale ...
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Reviews

Reviewer: Curtis Roger Cook

A mathematical framework for the measurement of internal attributes of software artifacts is presented. Concepts such as size, complexity, coupling, and cohesion have been introduced to define these attributes. Hundreds of quantitative measures have been proposed for these concepts. The contribution of this paper is to use the proposed framework to define necessary properties for these measurements. Such a framework is sorely needed because many of the proposed measures are based on intuition and hence are not well defined, which makes comparison and communication among researchers and practitioners difficult. The proposed framework is based on graph theory. A directed graph represents the system, and subgraphs correspond to modules. The authors illustrate the generality and precision of their framework by giving defining properties for size, length, complexity, cohesion, and coupling. For each concept, they give examples of current measures that satisfy and do not satisfy the software measurement properties. The authors also compare their defining sets of properties with other proposed defining sets of software complexity measures, including those of Weyuker, Fenton, and Zuse. They found much commonality as well as some differences. The intent of the paper was not to prove the validity of the proposed set of measurement properties, but to show its practicality and to sensitize software metrics researchers to the importance of a formal measurement framework. Metrics researchers should consider this when they define new software measures.

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Information & Contributors

Information

Published In

cover image IEEE Transactions on Software Engineering

IEEE Transactions on Software Engineering Volume 22, Issue 1

January 1996

93 pages

ISSN:0098-5589

Editor:
Richard A. Kemmerer
Univ. of California, Santa Barbara

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Copyright © Copyright © 1996 IEEE. All Rights Reserved.

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IEEE Press

Publication History

Published: 01 January 1996

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https://dl.acm.org/doi/10.1016/j.jss.2024.112143
Hasan MOsman MAdmodisastro NMuhammad M(2023)AI-based Quality-driven Decomposition Tool for Monolith to Microservice MigrationProceedings of the 2023 4th Asia Service Sciences and Software Engineering Conference10.1145/3634814.3634839(181-191)Online publication date: 27-Oct-2023
https://dl.acm.org/doi/10.1145/3634814.3634839
Kaur GPruthi JGandhi P(2023)Decision Tree Regression Analysis of Proposed Metric Suite for Software Fault PredictionSN Computer Science10.1007/s42979-023-02386-95:1Online publication date: 6-Dec-2023
https://dl.acm.org/doi/10.1007/s42979-023-02386-9
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