Learning inductive invariants by sampling from frequency distributions

Abstract

Automated verification for program safety is reduced to the discovery safe inductive invariants, i.e., formulas that over-approximate the sets of reachable program states, but precise enough to prove unreachability of the error state. We present a framework, called FreqHorn, that follows the Syntax-Guided Synthesis paradigm to iteratively sample candidate invariants from a formal grammar and check them with an SMT solver. FreqHorn automatically constructs grammars based on either source code or bounded proofs. After each (un-)successful candidate, FreqHorn adjusts the grammars to ensure the candidate is not sampled again. The process continues either until the conjunction of successful candidates (called lemmas) is sufficient, or the search space is exhausted. Additionally, FreqHorn keeps a history of counterexamples-to-induction (CTI) which block learning a lemma. With some periodicity, it checks if there is a CTI which is invalidated by the currently learned lemmas and rechecks the failed lemma if needed. FreqHorn is able to check several candidates at the same time to filter them effectively using the well known Houdini algorithm.