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PLDI 2020
Mon 15 - Fri 19 June 2020
Tue 16 Jun 2020 11:00 - 11:30 at MAPL live stream - Deep Learning and Program Verification Chair(s): Xujie Si

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 were previously manual proofs, automatically producing proofs for 28% of theorem statements in our test dataset, when combined with solver-based tooling. Without any additional solvers, we exhibit a proof completion rate that is a 4X improvement over prior state-of-the-art machine learning models for generating proofs in Coq.

Tue 16 Jun

Displayed time zone: Pacific Time (US & Canada) change

10:30 - 11:30
Deep Learning and Program VerificationMAPL at MAPL live stream
Chair(s): Xujie Si McGill University, Canada
An Abstraction-Based Framework for Neural Network Verification
Guy Katz Hebrew University
Generating Correctness Proofs with Neural Networks
Alex Sanchez-Stern University of California, San Diego, Yousef Alhessi University of California, San Diego, Lawrence Saul University of California, San Diego, Sorin Lerner University of California at San Diego, USA