Defect Prediction via LSTM Based on Sequence and Tree Structure

With the ever-expanding spread of contemporary software, software defect prediction (SDP) is attracting more and more attention. However, sequential networks used in previous studies, weaken syntactic information and fail to capture longdistance dependencies. To solve these problems, we develop a long short-term memory network based on bidirectional and tree structure (LSTM-BT). Specifically, LSTM-BT combines bidirectional long short-term memory networks (BI-LSTM) and tree long short-term memory networks (Tree-LSTM) to capture semantic and syntactic features from source codes. First, token vectors are captured from the abstract syntax tree (AST). Second, an embedding layer is used to extract semantic information hidden inside the AST nodes. Last, features are fed to the LSTM- BT, which is used to conduct predictions of defect-proneness. To validate our method, we carried out experiments on 8 pairs of Java open-source projects and the results show that LSTM- BT performs better compared to several state-of-the-art defect prediction models.