Correlation Metric Selection based Correlation Alignment for Cross-project Defect Prediction

Abstract

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.