No abstract

Software defect prediction (SDP) can find potential containing defect modules, which assists software developers in allocating limited test resources more efficiently. Because traditional software features fail to capture the semantics of source code, various studies have turned to extracting deep learning features. Existing related approaches often parse the program source code into Abstract Syntax Trees (ASTs) for further processing. However, most of these approaches ignore AST nodes' hierarchical and position-sensitive structure. To overcome the aforementioned issues, a two-stage AST encoding (TSE) method is proposed in this paper for software defect prediction. Experiments on eight Java open-source projects showed that our proposed SDP method outperforms several traditional methods and state-of-the-art deep learning methods in terms of F-measure and MCC.