On Abstraction Refinement for Bayesian Program Analysis

Bayesian program analysis is a systematic approach to learn from external information for better accuracy by converting logical deduction in conventional program analysis into Bayesian inference. A key challenge in Bayesian program analysis is how to select program abstractions to effectively generalize from external information. A recent approach addresses this challenge by learning a selection policy on training programs but may result in sub-optimal performance on new programs due to its learning nature and when the training set selection is not ideal. To address this problem, we propose an approach that is inspired by the framework of counterexample-guided refinement to search for an abstraction on the fly. Our key innovation is to apply the theory of conditional independence to refine the abstraction so that incorrect generalizations can be removed. To demonstrate the effectiveness of our approach, we have instantiated it on a Bayesian thread-escape analysis and a Bayesian datarace analysis and shown that it significantly improves the performance of the analyses.