Predicate Generation for Learning-Based Quantifier-Free Loop Invariant Inference
This work addresses a specific bottleneck in automated program verification for software engineers, but it is incremental as it builds on an existing learning-based algorithm.
The paper tackles the predicate generation problem in loop invariant inference by applying the interpolation theorem to synthesize predicates from program texts, improving the effectiveness and efficiency of a learning-based algorithm, with experiments on examples from Linux, SPEC2000, and Tar utility.
We address the predicate generation problem in the context of loop invariant inference. Motivated by the interpolation-based abstraction refinement technique, we apply the interpolation theorem to synthesize predicates implicitly implied by program texts. Our technique is able to improve the effectiveness and efficiency of the learning-based loop invariant inference algorithm in [14]. We report experiment results of examples from Linux, SPEC2000, and Tar utility.