ABSTRACT 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. INDEX TERMS Software defect prediction, cross-project defect prediction, feature selection, feature ranking.
Yu et al. REF performed an empirical study on the effectiveness of feature subset selection methods and feature ranking methods for CPDP.
An Empirical Study on the Effectiveness of Feature Selection for Cross-Project Defect Prediction
IEEE Access
IEEE Access
Computer Science