Categorisation‐based approach for predicting the fault‐proneness of object‐oriented classes in software post‐releases

Abstract

Subsequent releases of a system have common development environments and characteristics. However, prediction models based on within-project data potentially suffer from being based on fault data reported within relatively short maintenance time intervals, which potentially decreases their prediction abilities. In this study, the authors propose an approach that improves the classification performance of models based on within-project data that are applied to predict the fault-proneness of the classes in a software post-release (PR). The proposed approach involves selecting a set of immediate pre-releases and constructing a prediction model based on each pre-release. The PR classes are categorised based on whether they are newly developed or they are reused, with or without modification, from one or more of the selected pre-releases. The prediction models are applied to the PR classes reused from selected pre-releases, and the results are used to construct a fault-proneness prediction model. After applying this prediction model to all PR classes, the fault-proneness results are adjusted by considering the relationship between the prediction results of the individual pre-release models and the actual fault data. They reported an empirical study that shows that the classification performance of the categorisation-based fault-proneness prediction models is considerably better than those constructed using existing approaches.