Predicting fault-prone modules with case-based reasoning

Abstract

Software quality classification models seek to predict quality factors such as whether a module will be fault-prone, or not. Case-based reasoning (CBR) is a modeling technique that seeks to answer new questions by identifying similar `cases' from the past. When applied to software reliability, the working hypothesis of our approach is this: a module currently under development is probably fault-prone if a module with similar product and process attributes in an earlier release was fault-prone. The contribution of this paper is application of case-based reasoning to software quality modeling. To the best of our knowledge, this is the first time that case-based reasoning has been used to identify fault-prone modules. A case study illustrates our approach and provides evidence that case-based reasoning can be the basis for useful software quality classification models that are competitive with discriminant models. The case study revisits data from a previously published nonparametric discriminant analysis study. The Type II misclassification rate of the CBR model was substantially better than that of the discriminant model. Although the Type I misclassification rate was slightly greater and the overall misclassification rate was only slightly less, the CBR model was preferred when costs of misclassification were considered.