Automatic construction of an effective training set for prioritizing static analysis warnings

Abstract

In order to improve ineffective warning prioritization of static analysis tools, various approaches have been proposed to compute a ranking score for each warning. In these approaches, an effective training set is vital in exploring which factors impact the ranking score and how. While manual approaches to build a training set can achieve high effectiveness but suffer from low efficiency (i.e., high cost), existing automatic approaches suffer from low effectiveness. In this paper, we propose an automatic approach for constructing an effective training set. In our approach, we select three categories of impact factors as input attributes of the training set, and propose a new heuristic for identifying actionable warnings to automatically label the training set. Our empirical evaluations show that the precision of the top 22 warnings for Lucene, 20 for ANT, and 6 for Spring can achieve 100% with the help of our constructed training set.