文献类型：期刊文献

英文题名：Data sampling and kernel manifold discriminant alignment for mixed-project heterogeneous defect prediction

作者：Niu, Jingwen[1,2];Li, Zhiqiang[1];Chen, Haowen[3];Dong, Xiwei[4];Jing, Xiao-Yuan[3,5,6,7]

第一作者：Niu, Jingwen

通讯作者：Li, ZQ[1]

机构：[1]Shaanxi Normal Univ, Sch Comp Sci, Xian 710119, Peoples R China;[2]Xinxiang Univ, Sch Comp & Informat Engn, Xinxiang 453003, Henan, Peoples R China;[3]Wuhan Univ, Sch Comp Sci, Wuhan 430072, Peoples R China;[4]Jiujiang Univ, Sch Comp & Big Data Sci, Jiujiang 332005, Peoples R China;[5]Guangdong Univ Petrochem Technol, Sch Comp Sci, Maoming 525000, Peoples R China;[6]Guangdong Univ Petrochem Technol, Guangdong Prov Key Lab Petrochem Equipment Fault, Maoming 525000, Peoples R China;[7]Nanjing Univ, State Key Lab Novel Software Technol, Nanjing 210023, Peoples R China

第一机构：Shaanxi Normal Univ, Sch Comp Sci, Xian 710119, Peoples R China

通讯机构：[1]corresponding author), Shaanxi Normal Univ, Sch Comp Sci, Xian 710119, Peoples R China.

年份：0

外文期刊名：SOFTWARE QUALITY JOURNAL

收录：;EI(收录号：20221611963901);Scopus(收录号：2-s2.0-85127945142);WOS:【SCI-EXPANDED(收录号:WOS:000780783900001)】；

基金：The authors would like to thank the editors and anonymous reviewers for their constructive comments and suggestions. This work was partially supported by the National Natural Science Foundation of China under Grant Nos. 61902228, 62041603 and 62176069, the Natural Science Basic Research Program of Shaanxi Province under Grant No. 2020JQ-422, the Natural Science Foundation of Guangdong Province under Grant No. 2019A1515011076, the Innovation Group of Guangdong Education Department under Grant Nos. 2020KCXTD014 and 2018KCXTD019, the 2019 Key Discipline project of Guangdong Province, the Natural Science Foundation of Jiangxi Province under Grant No. 20202BABL202036, the Fundamental Research Funds for the Central Universities under Grant Nos. GK202103083 and GK202105006, and the project of State Key Laboratory for Novel Software Technology under Grant No. KFKT2021B29.

语种：英文

外文关键词：Heterogeneous defect prediction; Mixed project; Data sampling; Kernel manifold discriminant alignment; Software quality assurance

摘要：Heterogeneous defect prediction (HDP) refers to identifying more likely defect-proneness of software modules in a target project using heterogeneous metric data from other source projects, which solves the heterogeneous metric problem in cross-project defect prediction. Recently, several mixed-project HDP methods have been presented. However, these models neglect to address the linear inseparability and cross-project class imbalance issues simultaneously. These limitations usually lead to the unsatisfactory performance of HDP. In this paper, we propose an improved transfer learning approach for mixed-project HDP to deal with the above limitations, called data sampling and kernel manifold discriminant alignment (DSKMDA). DSKMDA firstly applies data sampling technique to handle the class imbalance issue. Then it uses kernel manifold discriminant alignment technique to handle the linear inseparability issue. Extensive experiments on 13 projects from three public benchmark datasets with four evaluation measures demonstrate that DSKMDA can produce better or comparable results against a range of competing methods.