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.
Fri 22 Jun Times are displayed in time zone: Eastern Time (US & Canada) change
14:00 - 15:40: Program AnalysisPLDI Research Papers at Grand Ballroom CD Chair(s): Isil DilligUT Austin | |||
14:00 - 14:25 Talk | Active Learning of Points-To Specifications PLDI Research Papers Osbert BastaniStanford University, Rahul SharmaMicrosoft Research, Alex AikenStanford University, Percy LiangStanford University Media Attached | ||
14:25 - 14:50 Talk | Pinpoint: Fast and Precise Sparse Value Flow Analysis for Million Lines of Code PLDI Research Papers Qingkai ShiHong Kong University of Science and Technology, China, Xiao XiaoSourceBrella Inc., Rongxin WuDepartment of Computer Science and Engineering, The Hong Kong University of Science and Technology, Jinguo ZhouSourcebrella Inc., Gang Fan, Charles Zhang Media Attached | ||
14:50 - 15:15 Talk | A Data-Driven CHC Solver PLDI Research Papers Media Attached | ||
15:15 - 15:40 Talk | User-Guided Program Reasoning using Bayesian Inference PLDI Research Papers Mukund RaghotamanUniversity of Pennsylvania, Sulekha KulkarniGeorgia Tech, Kihong HeoUniversity of Pennsylvania, USA, Mayur NaikUniversity of Pennsylvania Media Attached |