Generating Correctness Proofs with Neural Networks

Abstract

Foundational verification allows programmers to build software which has been empirically shown to have high levels of assurance in a variety of important domains. However, the cost of producing foundationally verified software remains prohibitively high for most projects, as it requires significant manual effort by highly trained experts. In this paper we present Proverbot9001 a proof search system using machine learning techniques to produce proofs of software correctness in interactive theorem provers. We demonstrate Proverbot9001 on the proof obligations from a large practical proof project, the CompCert verified C compiler, and show that it can effectively automate what was previously manual proofs, automatically solving 15.77% of proofs in our test dataset. This corresponds to an over 3X improvement over the prior state of the art machine learning technique for generating proofs in Coq.