Article

Building Software Quality Classification Trees: Approach, Experimentation, Evaluation

Authors:

Ryouei Takahashi,

Yoichi Muraoka,

Yukihiro NakamuraAuthors Info & Claims

ISSRE '97: Proceedings of the Eighth International Symposium on Software Reliability Engineering

Page 222

Published: 02 November 1997 Publication History

Abstract

A methodology for constructing an optimum software quality classification tree using software complexity metrics to discriminate between high-quality modules and low-quality modules is proposed. The process of tree generation is an application of the AIC (Akaike Information Criterion) procedures to the binomial distribution. AIC procedures are based on maximum likelihood estimation and the least number of complexity metrics. It is an improvement of the software quality classification tree generation method proposed by Porter and Selby from the viewpoint the complexity metrics are minimized. The problems of their method are that the software quality prediction model is unstable because it reflects observational errors in real data too much and there is no objective criterion for determining whether the discrimination is appropriate or not at a deep nesting level of the classification tree when the number of sample modules gets smaller. To solve these problems a new metric is introduced and its validity is theoretically and experimentally verified. In our examples, complexity metrics written in C language such as lines of source code, Halstead's software science, McCabe's cyclomatic number, Henry and Kafura's fan-in/out, Howatt and Baker's s cope number and reuse-ratio, are investigated. Our experiments with a medium-sized piece of software (85 thousand lines of source code: 562 samples) show that the software quality classification tree generated by our new metric identifies the target class of the observed modules more efficiently using the minimum number of complexity metrics without any decrease of the correct classification ratio (76%->72%) than the conventional classification tree.

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Cited By

Kim BAl-Nashif YFayssal SHariri SYousif MParashar MLauria MMcCann JCotroneo D(2008)Anomaly-based fault detection in pervasive computing systemProceedings of the 5th international conference on Pervasive services10.1145/1387269.1387294(147-156)Online publication date: 6-Jul-2008
https://dl.acm.org/doi/10.1145/1387269.1387294
Khoshgoftaar TSeliya NGao K(2005)Assessment of a New Three-Group Software Quality Classification TechniqueEmpirical Software Engineering10.1007/s10664-004-6191-x10:2(183-218)Online publication date: 1-Apr-2005
https://dl.acm.org/doi/10.1007/s10664-004-6191-x
Khoshgoftaar TSeliya N(2004)Comparative Assessment of Software Quality Classification TechniquesEmpirical Software Engineering10.1023/B:EMSE.0000027781.18360.9b9:3(229-257)Online publication date: 1-Sep-2004
https://dl.acm.org/doi/10.1023/B%3AEMSE.0000027781.18360.9b
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cover image Guide Proceedings

ISSRE '97: Proceedings of the Eighth International Symposium on Software Reliability Engineering

November 1997

ISBN:0818681209

Copyright © Copyright (c) 1997 Institute of Electrical and Electronics Engineers, Inc. All rights reserved.

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IEEE Computer Society

United States

Publication History

Published: 02 November 1997

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Cited By

Kim BAl-Nashif YFayssal SHariri SYousif MParashar MLauria MMcCann JCotroneo D(2008)Anomaly-based fault detection in pervasive computing systemProceedings of the 5th international conference on Pervasive services10.1145/1387269.1387294(147-156)Online publication date: 6-Jul-2008
https://dl.acm.org/doi/10.1145/1387269.1387294
Khoshgoftaar TSeliya NGao K(2005)Assessment of a New Three-Group Software Quality Classification TechniqueEmpirical Software Engineering10.1007/s10664-004-6191-x10:2(183-218)Online publication date: 1-Apr-2005
https://dl.acm.org/doi/10.1007/s10664-004-6191-x
Khoshgoftaar TSeliya N(2004)Comparative Assessment of Software Quality Classification TechniquesEmpirical Software Engineering10.1023/B:EMSE.0000027781.18360.9b9:3(229-257)Online publication date: 1-Sep-2004
https://dl.acm.org/doi/10.1023/B%3AEMSE.0000027781.18360.9b
Khoshgoftaar TSeliya N(2003)Fault Prediction Modeling for Software Quality EstimationEmpirical Software Engineering10.1023/A:10244248113458:3(255-283)Online publication date: 1-Sep-2003
https://dl.acm.org/doi/10.1023/A%3A1024424811345
Sato SMonden AMatsumoto KAoyama MInoue KRajlich V(2002)Evaluating the applicability of reliability prediction models between different softwareProceedings of the International Workshop on Principles of Software Evolution10.1145/512035.512057(97-102)Online publication date: 19-May-2002
https://dl.acm.org/doi/10.1145/512035.512057
Khoshgoftaar TYuan XAllen EJones WHudepohl J(2002)Uncertain Classification of Fault-Prone Software ModulesEmpirical Software Engineering10.1023/A:10205110042677:4(297-318)Online publication date: 1-Dec-2002
https://dl.acm.org/doi/10.1023/A%3A1020511004267
Abd-El-Hafiz S(2001)Entropies as Measures of Software InformationProceedings of the IEEE International Conference on Software Maintenance (ICSM'01)10.1109/ICSM.2001.972721Online publication date: 7-Nov-2001
https://dl.acm.org/doi/10.1109/ICSM.2001.972721
Khoshgoftaar TAllen EJones WHudepohl J(2001)Data Mining of Software Development DatabasesSoftware Quality Journal10.1023/A:10133494195459:3(161-176)Online publication date: 1-Nov-2001
https://dl.acm.org/doi/10.1023/A%3A1013349419545
Khoshgoftaar TAllen E(2001)Controlling Overfitting in Classification-Tree Models ofSoftware QualityEmpirical Software Engineering10.1023/A:10098030045766:1(59-79)Online publication date: 1-Mar-2001
https://dl.acm.org/doi/10.1023/A%3A1009803004576
Khoshgoftaar TShan RAllen E(2000)Improving Tree-Based Models of Software Quality with Principal Components AnalysisProceedings of the 11th International Symposium on Software Reliability Engineering10.5555/851024.856223Online publication date: 8-Oct-2000
https://dl.acm.org/doi/10.5555/851024.856223
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