An Empirical Study on the Effectiveness of Feature Selection for Cross-Project Defect Prediction

IEEE Access

Junyan Qian¹  Shujuan Jiang²  Gongjie Zhang³  Qiao Yu³  Zhenhua Wu⁴ 

[1] Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, China;School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China;School of Computer Science and Technology, Jiangsu Normal University, Xuzhou, China;School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, China;

关键词: Software defect prediction;    cross-project defect prediction;    feature selection;    feature ranking;   

DOI  :  10.1109/ACCESS.2019.2895614

来源: DOAJ

【 摘 要 】

Software defect prediction has attracted much attention of researchers in software engineering. At present, feature selection approaches have been introduced into software defect prediction, which can improve the performance of traditional defect prediction (known as within-project defect prediction, WPDP) effectively. However, the studies on feature selection are not sufficient for cross-project defect prediction (CPDP). In this paper, we use the feature subset selection and feature ranking approaches to explore the effectiveness of feature selection for CPDP. An empirical study is conducted on NASA and PROMISE datasets. The results show that both the feature subset selection and feature ranking approaches can improve the performance of CPDP. Therefore, we should select the representative feature subset or set a reasonable proportion of selected features to improve the performance of CPDP in future studies.

【 授权许可】

Unknown