The Detection of Fault-Prone Programs
IEEE Transactions on Software Engineering
Predicting Fault-Prone Software Modules in Telephone Switches
IEEE Transactions on Software Engineering
Using Formal Description Techniques: An Introduction to Estelle, Lotos, and SDL
Using Formal Description Techniques: An Introduction to Estelle, Lotos, and SDL
Software Quality Management
Uncertain Classification of Fault-Prone Software Modules
Empirical Software Engineering
Quantitative Analysis of Faults and Failures in a Complex Software System
IEEE Transactions on Software Engineering
Improving Tree-Based Models of Software Quality with Principal Components Analysis
ISSRE '00 Proceedings of the 11th International Symposium on Software Reliability Engineering
Modeling Fault-Prone Modules of Subsystems
ISSRE '00 Proceedings of the 11th International Symposium on Software Reliability Engineering
Analogy-Based Practical Classification Rules for Software Quality Estimation
Empirical Software Engineering
Comparative Assessment of Software Quality Classification Techniques: An Empirical Case Study
Empirical Software Engineering
Journal of Systems and Software
Software defect prediction using artificial immune recognition system
SE'07 Proceedings of the 25th conference on IASTED International Multi-Conference: Software Engineering
Improving fault detection in modified code: a study from the telecommunication industry
Journal of Computer Science and Technology
Reducing overfitting in genetic programming models for software quality classification
HASE'04 Proceedings of the Eighth IEEE international conference on High assurance systems engineering
Review: Software fault prediction: A literature review and current trends
Expert Systems with Applications: An International Journal
Software fault prediction for object oriented systems: a literature review
ACM SIGSOFT Software Engineering Notes
Evaluation of three methods to predict project success: a case study
PROFES'05 Proceedings of the 6th international conference on Product Focused Software Process Improvement
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Prediction of fault-prone modules provides one way to support software quality engineering through improved scheduling and project control. The primary goal of our research was to develop and refine techniques for early prediction of fault-prone modules. The objective of this paper is to review and improve an approach previously examined in the literature for building prediction models, i.e. principal component analysis (PCA) and discriminant analysis (DA). We present findings of an empirical study at Ericsson Telecom AB for which the previous approach was found inadequate for predicting the most fault-prone modules using software design metrics. Instead of dividing modules into fault-prone and not-fault-prone, modules are categorized into several groups according to the ordered number of faults. It is shown that the first discriminant coordinates (DC) statistically increase with the ordering of modules, thus improving prediction and prioritization efforts. The authors also experienced problems with the smoothing parameter as used previously for DA. To correct this problem and further improve predictability, separate estimation of the smoothing parameter is shown to be required.