Accelerating syntax-guided invariant synthesis

Abstract

We present a fast algorithm for syntax-guided synthesis of inductive invariants which combines enumerative learning with inductive-subset extraction, leverages counterexamples-to-induction and interpolation-based bounded proofs. It is a variant of a recently proposed probabilistic method, called FreqHorn, which is however less dependent on heuristics than its predecessor. We present an evaluation of the new algorithm on a large set of benchmarks and show that it exhibits a more predictable behavior than its predecessor, and it is competitive to the state-of-the-art invariant synthesizers based on Property Directed Reachability.