Software Defect Prediction: Heuristics for Weighted Naïve Bayes

Defect prediction is an important topic in software quality research. Statistical models for defect prediction can be built on project repositories. Project repositories store software metrics and defect information. This information is then matched with software modules. Naïve Bayes is a well known, simple statistical technique that assumes the ‘independence’ and ‘equal importance’ of features, which are not true in many problems. However, Naïve Bayes achieves high performances on a wide spectrum of prediction problems. This paper addresses the ‘equal importance’ of features assumption of Naïve Bayes. We propose that by means of heuristics we can assign weights to features according to their importance and improve defect prediction performance. We compare the weighted Naïve Bayes and the standard Naïve Bayes predictors’ performances on publicly available datasets. Our experimental results indicate that assigning weights to software metrics increases the prediction performance significantly.