
QED at Large: A Survey of Engineering of Formally Verified Software
Development of formal proofs of correctness of programs can increase act...
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Identifying SelfAdmitted Technical Debts with Jitterbug: A Twostep Approach
Keeping track of and managing the selfadmitted technical debts (SATDs) ...
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Zerocost metaprogrammed stateful functors in F*
Writing code is hard; proving it correct is even harder. As the scale of...
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Formally Verified Argument Reduction with a FusedMultiplyAdd
Cody & Waite argument reduction technique works perfectly for reasonably...
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Online Machine Learning Techniques for Coq: A Comparison
We present a comparison of several online machine learning techniques fo...
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Verification of ML Systems via Reparameterization
As machine learning is increasingly used in essential systems, it is imp...
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Lassie: HOL4 Tactics by Example
Proof engineering efforts using interactive theorem proving have yielded...
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Generating Correctness Proofs with Neural Networks
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 dataset. This corresponds to an over 3X improvement over the prior state of the art machine learning technique for generating proofs in Coq.
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