PathPair2Vec: An AST path pair-based code representation method for defect prediction

Software project defect prediction (SDP) can predict the bug probability of software by their features and allocate their testing efforts. The existing software defect prediction methods can be divided into two categories: methods based on traditional handcrafted features and methods based on automatically made abstract features, especially those made by deep learning. The current research indicates that deep learning-based automatic features can achieve better performance than handcrafted features. Code2vec (Alon et al. 2019) is one of the best source code representation models, which leverages deep learning to learn automatic representations from code. In this paper, inspired by code2vec, we propose a new AST path pair-based source code representation method (PathPair2Vec) and apply it to software project defect prediction. We first propose the concept of the short path to describe each terminal node and its control logic. Then, we design a new sequence encoding method to code the different parts of the terminal node and its control logic. Finally, by pairs of short paths, we describe the semantic information of code and fuse them by an attention mechanism. Experiments on the PROMISE dataset show that our method improves the F1 score by 17.88% over the state-of-the-art SDP method, and the AST path pair-based source code representation can better identify the defect features of the source code.