Jinyung Kim  Kwangkeun Yi

10.5626/JOK.2019.46.4.363

static program analysis path-sensitivity data-driven program analysis Machine Learning

We propose a data-driven method to improve path-sensitive industrial-strength static analyzers. Most industrial static analyzers adopt path-sensitive techniques and path selection holds the key to their performance. We propose a method to automatically learn new cost-effective path-selection heuristics from an existing analyzer with a manually tuned path-selection heuristic. We evaluated our method on an industrial static C code bug-finder from Sparrow as a baseline analyzer with 17 C open-source benchmark programs. The experimental results showed that with the newly-learned path-selection heuristic, the analyzer reported 90.8% of the defects in only 38% of the analysis time, compared to the baseline analysis. This method reported more defects in less time than the baseline path-selection heuristic under similar path search space constraints.