Cross-Project Defect Prediction Considering Multiple Data Distribution Simultaneously

Abstract

Software testing is the main method for finding software defects at present, and symmetric testing and other methods have been widely used, but these testing methods will cause a lot of waste of resources. Software defect prediction methods can reasonably allocate testing resources by predicting the defect tendency of software modules. Cross-project defect prediction methods have huge advantages when faced with missing datasets. However, most cross-project defect prediction methods are designed based on the settings of a single source project and a single target project. As the number of public datasets continues to grow, the number of source projects and defect information is increasing. Therefore, in the case of multi-source projects, this paper explores the problems existing when using multi-source projects for defect prediction. There are two problems. First, in practice, it is not possible to know in advance which source project is used to build the model to obtain the best prediction performance. Second, if an inappropriate source project is used in the experiment to build the model, it can lead to lower performance issues. According to the problems found in the experiment, the paper proposed a multi-source-based cross-project defect prediction method MSCPDP. Experimental results on the AEEEM dataset and PROMISE dataset show that the proposed MSCPDP method effectively solves the above two problems and outperforms most of the current state-of-art cross-project defect prediction methods on F1 and AUC. Compared with the six cross-project defect prediction methods, the F1 median is improved by 3.51%, 3.92%, 36.06%, 0.49%, 17.05%, and 9.49%, and the ACU median is improved by −3.42%, 8.78%, 0.96%, −2.21%, −7.94%, and 5.13%.