Feedback-driven Points-to Analysis

Points-to analysis is a static program analysis that extracts reference information from agiven input program. Its accuracy is limited due to abstractions that any such analysisneeds to make. Further, the exact analysis results are unknown, i.e., no so-called GoldStandard exists for points-to analysis. This hinders the assessment of new ideas to pointstoanalysis, as results can be compared only relative to results obtained by other inaccurateanalyses. In this paper, we present feedback-driven points-to analysis. We suggest performing(any classical) points-to analysis with the points-to results at certain programpoints guarded by a-priori upper bounds. Such upper bounds can come from other pointstoanalyses – this is of interest when different approaches are not strictly ordered in termsof accuracy – and from human insight, i.e., manual proofs that certain points-to relationsare infeasible for every program run. This gives us a tool at hand to compute very accuratepoints-to analysis and, ultimately, to manually create a Gold Standard