A Cross-Project Defect Prediction Model Based on Deep Learning With Self-Attention

Cross-project defect prediction technique is a hot topic in the field of software defect research because of the huge difference in data distribution between source project and target project. Software defect prediction technique usually first extracts software project features and then trains prediction models based on various classifiers. However, traditional features lack sufficient semantic information of source code resulting in poor performance of the prediction models. To construct more accurate prediction models based on the semantic information, we propose a cross-project defect prediction framework named BSLDP, which extracts semantic information of source code files through a bidirectional long and short-term memory network with self-attention mechanism. In particular, we provide semantic extractor named ALC to extract source code semantics based on source code files, and propose classification algorithm based on the semantic information of source project and target project, namely BSL, to build a prediction model. Furthermore, we propose an equal meshing mechanism that ALC generates semantic information on small fragments by dividing the numerical token vector to further improve the performance of the proposed model. We evaluated the performance of the proposed model on a publicly available PROMISE dataset. Compared with the four state-of-the-art methods, the experimental results indicate that on average BSLDP improves the performance of cross-project defect prediction in terms of F1 by 14.2%, 34.6%, 32.2% and 23.6%, respectively.