An empirical evaluation of fault-proneness models

• Published in: Proceedings - International Conference on Software Engineering pp. 241 - 251
• Main Authors: Denaro, Giovanni, Pezzè, Mauro
• Format: Conference Proceeding Journal Article
• Language: English
• Published: New York, NY, USA ACM 01.01.2002
• Series: ACM Conferences
• Subjects: Applied sciences; Computer science; control theory; systems; Exact sciences and technology; Software; Software and its engineering > Software creation and management > Software verification and validation > Software defect analysis > Software testing and debugging; Software and its engineering > Software organization and properties > Software functional properties > Formal methods > Model checking; Software engineering; Theory of computation > Logic > Verification by model checking; Theory of computation > Semantics and reasoning > Program reasoning > Program verification; testing process; cross-validation; fault-proneness models; logistic regression; principal component analysis; software faultiness; software metrics; software process; Logistic regression; Software metrics; Test; Software process improvement; Resource allocation; Software package; Cross validation; Multivariate analysis; Principal component analysis
• ISBN: 158113472X; 9781581134728
• ISSN: 0270-5257
• DOI: 10.1145/581339.581371

Planning and allocating resources for testing is difficult and it is usually done on empirical basis, often leading to unsatisfactory results. The possibility of early estimating the potential faultiness of software could be of great help for planning and executing testing activities. Most research concentrates on the study of different techniques for computing multivariate models and evaluating their statistical validity, but we still lack experimental data about the validity of such models across different software applications.This paper reports an empirical study of the validity of multivariate models for predicting software fault-proneness across different applications. It shows that suitably selected multivariate models can predict fault-proneness of modules of different software packages.