Program analyses necessarily make approximations that often lead them to report true alarms interspersed with many false alarms. We propose a new approach to leverage user feedback to guide program analyses towards true alarms and away from false alarms. Our approach associates each alarm with a confidence value by performing Bayesian inference on a probabilistic model derived from the analysis rules. In each iteration, the user inspects the alarm with the highest confidence and labels its ground truth, and the approach recomputes the confidences of the remaining alarms given this feedback. It thereby maximizes the return on the effort by the user in inspecting each alarm. We have implemented our approach in a tool named Bingo for program analyses expressed in Datalog. Experiments with real users and two sophisticated analyses---a static datarace analysis for Java programs and a static taint analysis for Android apps---show significant improvements on a range of metrics, including false alarm rates and number of bugs found.

Practical programs share large modules of code. However, many program analyses are ineffective at reusing analysis results for shared code across programs. We present POLYMER, an analysis optimizer to address this problem. POLYMER runs the analysis offline on a corpus of training programs and learns analysis facts over shared code. It prunes the learnt facts to eliminate intermediate computations and then reuses these pruned facts to accelerate the analysis of other programs that share code with the training corpus. We have implemented POLYMER to accelerate analyses specified in Datalog, and apply it to optimize two analyses for Java programs: a call-graph analysis that is flow- and context-insensitive, and a points-to analysis that is flow- and context-sensitive. We evaluate the resulting analyses on ten programs from the DaCapo suite that share the JDK library. POLYMER achieves average speedups of 2.6× for the call- graph analysis and 5.2× for the points-to analysis.