A differential evolution-based approach for effort-Aware just-in-Time software defect prediction

Abstract

Software defect prediction technology is an effective method to improve software quality. Effort-Aware just-in-Time software defect prediction (JIT-SDP) aims to identify more defective changes in limited effort. Although many methods have been proposed for JIT-SDP, the prediction performance of existing prediction models still needs to be improved. To improve the effort-Aware prediction performance, we propose a new method called DEJIT based on differential evolution algorithm. First, we propose a metric called density-percentile-Average (DPA), which is used as the optimization objective of models on the training set. Then, we use logistic regression to build models and use the differential evolution algorithm to determine coefficients of logistic regression. We conduct empirical research on six open source projects. Empirical results demonstrate that the proposed method significantly outperforms the state-of-The-Art 4 supervised models and 4 unsupervised models.