A Large-Scale Empirical Study of Actionable Warning Distribution Within Projects

Static Analysis Tools (SATs) show potential defect detection ability while their usability is severely hindered by massive unactionable warnings. To improve the usability of SATs, many machine learning-based Actionable Warning Identification (AWI) studies have been proposed, which mainly focus on mining warning features and improving identification models to identify actionable warnings. However, the underlying distribution of the warning dataset, which is closely related to feature mining and thereby affects AWI model performance, is not well-explored by these studies. Further, there is a lack of a well-prepared warning dataset to support the distribution analysis. In this article, we first propose a warning dataset construction approach, which incorporates manual inspection and verification latency into postprocess labels from an advanced closed-warning heuristic and thereby acquire credible labels. Based on 10 large-scale and real-world projects with 25K+ revisions and 2087K+ SpotBugs warnings, we construct a qualified warning dataset with 11975 distinct warnings. Subsequently, we thoroughly analyze the actionable warning distribution within projects against our constructed dataset from six warning characteristics (i.e., category, type, priority, rank, file, and method). Based on the analysis results, we present 16 findings. Finally, a preliminary study demonstrates that our findings can be practical and instructive in improving the usability of SATs.