Research on Software Defect Prediction Models Combining Static Analysis Warnings

Static analysis warnings, as an important software quality metric, are widely used to identify potential violations in the source code. Recent studies have shown that static analysis warnings are applied in code smell detection and just-in-time defect prediction, but they are not involved in early projects when commit logs are lacking. To address this   issue, this paper utilizes warning information from three popular static analysis tools, and combines it into the existing   defect prediction model. The paper creates a new metric covering both software development and code maintainability, and explores the potential relationship between static analysis warnings and defects. The paper also investigates the impact of combining warnings on the performance of software defect prediction models, and evaluates their influence in cross-project scenarios. The experimental results indicate that the quantity of warnings is closely related to the distribution of defects, showing a positive correlation. This suggests that warnings have significant potential in software defect prediction models, and the reported warning information in datasets with defects is often related to coding standards. After combining warnings, the defect prediction model improves the average precision by 1.4% to 14.7%, the average recall by 0.2% to 2.4%, the average F1 by 0.3% to 3.0%, and the average AUC by 0.2% to 1.4% in different projects. In cross-project scenarios, the metric CODE+SAW_VIF provides the best-performing defect prediction model, and combining static analysis warnings enhances the model’s ability to identify defects.