A Method-Level Defect Prediction Approach Based on Structural Features of Method-Calling Network

Abstract

Software defect prediction models help testers find program modules that have a high probability of having defects. A method-calling network can express the dependencies between methods in a program. Existing approaches do not sufficiently utilize method-calling network to characterize the structural features between methods. To address this problem, in this study, it is proposed for the first time that the characteristics of methods in a program are obtained by analyzing the method-calling network, and a new approach is proposed for defect prediction at the method-level. Specifically in this study, the method-calling network of the program was first constructed and the network metrics of the method-calling network were obtained. Next, the new network embedding technique (node2vec) was used to automatically encode the method-calling network structure into a low-dimensional vector to obtain the network embedding metrics. Finally, they were combined with code metrics to construct defect prediction models. We evaluated our approaches on 13 open-source software systems. The experimental results show that the proposed method improved the values of area under the receiver operating characteristic curve by 2.5% to 6.7% and Matthews correlation coefficient by 13% to 178.4% compared to the baselines. Therefore the method-calling network contains rich structural features between methods, and the structural features extend the features used for method-level defect prediction and further improve the performance of defect prediction models.