SyntaxLineDP: a Line-level Software Defect Prediction Model based on Extended Syntax Information.

The existence of software defects greatly restricts the application of software and may bring great economic losses. Defect prediction techniques based on file level or module level can help code reviewers quickly locate defects and fix them. In recent years, researchers have used natural language techniques to explore the defectiveness of code lines by analyzing the semantic information of the code lines. However, the appearance of defects is often associated with the syntactic information of the code. To fully use the syntax information associated with code lines, we propose a code line-level representation considering the coverage of the syntax node. Specifically, we add the syntax node to its corresponding coverage lines. Combining with the BiLSTM model, we propose a new line-level defect prediction model, SyntaxLineDP, which shows good performance achieving 0.72 and 0.62 for AUC and Balanced Accuracy respectively. SyntaxLineDP outperforms the state-of-the-art model and the popular static tools (e.g., ErrorProne, PMD). Specifically, In the within-project evaluation, the SyntaxLineDP model outperforms the start-of-the-art model by 15% and 27% on the Recall@Top20% LOC metric and the Effort@Top20% Recall metric respectively. In the cross-project evaluation, the SyntaxLineDP outperforms the start-of-the-art model by 7% and 42% on the Recall@Top20% LOC metric and the Effort@Top20% Recall metric respectively. Through ablation experiments, we studied how the selection of extended syntax information contributes to the detection and found out some key features that are helpful for the defect prediction task: number of infix expressions of code line, number of blocks of code line, and number of method invocation of the code line.