文献类型：期刊文献

英文题名：A differential evolution-based approach for effort-Aware just-in-Time software defect prediction

作者：Yang, Xingguang[1]; Yu, Huiqun[2]; Fan, Guisheng[3]; Yang, Kang[3]

机构：[1] East China University of Science and Technology, Shanghai Key Laboratory of Computer Software Evaluating and Testing, Shanghai, China; [2] East China University of Science and Technology, Shanghai Engineering Research Center of Smart Energy, Shanghai, China; [3] East China University of Science and Technology, Shanghai, China

年份：2020

起止页码：13

外文期刊名：RL+SE and PL 2020 - Proceedings of the 1st ACM SIGSOFT International Workshop on Representation Learning for Software Engineering and Program Languages, Co-located with ESEC/FSE 2020

收录：EI(收录号：20204909576035)

语种：英文

外文关键词：Just in time production - Computer software selection and evaluation - Evolutionary algorithms - Forecasting - Open source software - Optimization

摘要：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. ? 2020 ACM.