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interpretation is a powerful tool in program verification. Several commercial or industrial scale implementations of abstract interpretation have demonstrated that this approach can verify safety properties of real-world code. However, using abstract interpretation tools is not always simple. If no user-provided hints are available, the abstract interpretation engine may lose precision during widening and produce an overwhelming number of false alarms. However, manually providing these hints is time consuming and often frustrating when re-running the analysis takes a lot of time. We present an algorithm for program verification that combines abstract interpretation, symbolic execution and crowdsourcing. If verification fails, our procedure suggests likely invariants, or program patches, that provide helpful information to the verification engineer and makes it easier to find the correct specification. By complementing machine learning with well-designed games, we enable program analysis to incorporate human insights that help improve their scalability and usability.

Invariant discovery is one of the central problems in software verification. This paper reports on an approach that addresses this problem in a novel way; it crowdsources logical expressions for likely invariants by turning invariant discovery into a computer game. The game, called Binary Fission, employs a classification model. In it, players compose preconditions by separating program states that preserve or violate program assertions. The players have no special expertise in formal methods or programming, and are not specifically aware they are solving verification tasks. We show that Binary Fission players discover concise, general, novel, and human readable program preconditions. Our proof of concept suggests that crowdsourcing offers a feasible and promising path towards the practical application of verification technology.

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