文献类型：期刊文献

英文题名：DEJIT: A Differential Evolution Algorithm for Effort-Aware Just-in-Time Software Defect Prediction

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

机构：[1]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China;[2]Shanghai Key Lab Comp Software Evaluating & Testi, Shanghai 201112, Peoples R China;[3]Shanghai Engn Res Ctr Smart Energy, Shanghai, Peoples R China

年份：2021

卷号：31

期号：03

起止页码：289

外文期刊名：INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING

收录：;EI(收录号：20211510206996);WOS:【SCI-EXPANDED(收录号:WOS:000636815500001)】；

基金：This work is partially supported by the NSF of China under grants Nos. 61772200 and 61702334, Shanghai Pujiang Talent Program under grants No. 17PJ1401900. Shanghai Municipal Natural Science Foundation under Grants Nos. 17ZR1406900 and 17ZR1429700. Educational Research Fund of ECUST under Grant No. ZH1726108. The Collaborative Innovation Foundation of Shanghai Institute of Technology under Grants No. XTCX2016-20.

语种：英文

外文关键词：Software defect prediction; just-in-time; differential evolution; empirical software engineering

摘要：Software defect prediction is an effective approach to save testing resources and improve software quality, which is widely studied in the field of software engineering. The effort-aware just-in-time software defect prediction (JIT-SDP) aims to identify defective software changes in limited software testing resources. Although many methods have been proposed to solve the JIT-SDP, the effort-aware prediction performance of the existing models still needs to be further improved. To this end, we propose a differential evolution (DE) based supervised method DEJIT to build JIT-SDP models. Specifically, first we propose a metric called density-percentile-average (DPA), which is used as optimization objective on the training set. Then, we use logistic regression (LR) to build a prediction model. To make the LR obtain the maximum DPA on the training set, we use the DE algorithm to determine the coefficients of the LR. The experiment uses defect data sets from six open source projects. We compare the proposed method with state-of-the-art four supervised models and four unsupervised models in cross-validation, cross-project-validation and timewise-cross-validation scenarios. The empirical results demonstrate that the DEJIT method can significantly improve the effort-aware prediction performance in the three evaluation scenarios. Therefore, the DEJIT method is promising for the effort-aware JIT-SDP.