文献类型：会议论文

英文题名：Correlation Metric Selection based Correlation Alignment for Cross-project Defect Prediction

作者：Niu, Jingwen[1,2];Li, Zhiqiang[1];Qi, Chao[1]

第一作者：Niu, Jingwen

通讯作者：Li, ZQ[1]

机构：[1]Shaanxi Normal Univ, Sch Comp Sci, Xian, Peoples R China;[2]Xinxiang Univ, Sch Comp & Informat Engn, Xinxiang, Henan, Peoples R China

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

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

会议论文集：20th Int Conf on Ubiquitous Comp and Communicat (IUCC) / 20th Int Conf on Comp and Information Technology (CIT) / 4th Int Conf on Data Science and Computational Intelligence (DSCI) / 11th Int Conf on Smart Computing, Networking, and Serv (SmartCNS)

会议日期：DEC 20-22, 2021

会议地点：ELECTR NETWORK

语种：英文

外文关键词：Cross-project defect prediction; correlation alignment; correlation based metric selection; domain adaptation; software quality assurance

摘要：Cross-project defect prediction (CPDP) aims to identify defective software modules in a target project by using historical defect data from other source projects. Recently, CPDP has attracted much more research interest. However, existing CPDP models are parametric methods, which usually require intensive parameter selection and tuning to achieve better prediction performance. Moreover, software metrics (features) usually have strong correlation among themselves and this is detrimental to build prediction models. However, most CPDP methods don't consider to reduce correlated metrics, which may bring negative effect on the performance of CPDP. In the article, we proposed a new Correlation Metric Selection based Correlation Alignment (CMSCA) approach for CPDP to address the above concerns. Specifically, CMSCA is a non-parametric algorithm, which can perform knowledge transfer across projects without the need for parameter selection and tuning. It is simple but effective. Through an empirical investigation of 5 publicly-available defect datasets, experimental results demonstrate that the proposed CMSCA model outperforms or has comparable to the related CPDP models.