Classification of Fault-Prone Software Modules: Prior Probabilities,Costs, and Model Evaluation
Empirical Software Engineering
Detection of software modules with high debug code churn in a very large legacy system
ISSRE '96 Proceedings of the The Seventh International Symposium on Software Reliability Engineering
Predicting Fault-Prone Modules with Case-Based Reasoning
ISSRE '97 Proceedings of the Eighth International Symposium on Software Reliability Engineering
Fault-prone module prediction of a web application using artificial neural networks
SEA '07 Proceedings of the 11th IASTED International Conference on Software Engineering and Applications
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Assessment of large complex systems requires robust modeling techniques. Multivariate models can be misleading if the underlying metrics are highly correlated. Munson and Khoshgoflaar propose using principal components analysis to avoid such problems. Even though many have used the technique, the advantages have not previously been empirically demonstrated, especially for large complex systems. Our case study illustrates that principal components analysis can substantially improve the predictive quality of a software quality model. This paper presents a case study of a sample of modules representing about 1.3 million lines of code, taken from a much larger real-time telecommunications system. This study used discriminant analyse's for classification of fault-prone modules, based on measurements of software design attributes and categorical variables indicating new, changed, and reused modules. Quality of fit and predictive quality were evaluated.