Software bug prediction using weighted majority voting techniques

Mining software repositories is a growing research field where rich data available in the different development software repositories, are analyzed and cross-linked to uncover useful information. Bug prediction is one of the potential benefits that can be gained through mining software repositories. Predicting potential defects early as they are introduced to the version control system would definitely help in saving time and effort during testing or maintenance phases. In this paper, defect prediction models that uses ensemble classification techniques have been proposed. The proposed models have been applied using different sets of software metrics as attributes of the classification techniques and tested on datasets of different sizes. The results show that change metrics outperform static code metrics and the combined model of change and static code metrics. Ensembles tend to be more accurate than their base classifiers. Defect prediction models using change metrics and ensemble classifiers have revealed the best performance, especially when the datasets used have imbalanced class distribution.