Resumen de Selective X-Sensitive Analysis Guided by Impact Pre-Analysis
Hakjoo Oh, Wonchan Lee, Kihong Heo, Hongseok Yang, Kwangkeun Yi
We present a method for selectively applying context-sensitivity during interprocedural program analysis. Our method applies context-sensitivity only when and where doing so is likely to improve the precision that matters for resolving given queries. The idea is to use a pre-analysis to estimate the impact of context-sensitivity on the main analysis’s precision, and to use this information to find out when and where the main analysis should turn on or off its context-sensitivity. We formalize this approach and prove that the analysis always benefits from the pre-analysis--guided context-sensitivity. We implemented this selective method for an existing industrial-strength interval analyzer for full C. The method reduced the number of (false) alarms by 24.4% while increasing the analysis cost by 27.8% on average.
The use of the selective method is not limited to context-sensitivity. We demonstrate this generality by following the same principle and developing a selective relational analysis and a selective flow-sensitive analysis. Our experiments show that the method cost-effectively improves the precision in the these analyses as well.
