Synthesizing Implication Lemmas for Interactive Theorem Proving
Ana Brendel, Aishwarya Sivaraman, Todd D. Millstein
摘要
Interactive theorem provers (ITP) enable programmers to formally verify properties of their software systems. One burden for users of ITPs is identifying the necessary helper lemmas to complete a proof, for example those that define key inductive invariants. Existing approaches to lemma synthesis for ITPs have limited, if any, support for synthesizing implications: lemmas of the form 𝑃 1 ∧ • • • ∧ 𝑃 𝑛 =⇒ 𝑄. In this paper, we propose a technique and associated tool for synthesizing useful implication lemmas. Our approach employs a form of data-driven invariant inference to explore strengthenings of the current proof state, based on sample valuations of the current goal and assumptions. We have implemented our approach in a Rocq tactic called dilemma. We demonstrate its effectiveness in synthesizing necessary helper lemmas for proofs from the Verified Functional Algorithms textbook as well as from prior benchmark suites for lemma synthesis.
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- TacTok: semantics-aware proof synthesisEmily First, Yuriy Brun, Arjun GuhaOOPSLA 2020 · 被引用 39 次
- Data-driven inference of representation invariantsAnders Miltner, Saswat Padhi, Todd D. Millstein, David WalkerPLDI 2020 · 被引用 33 次
- Data-driven lemma synthesis for interactive proofsAishwarya Sivaraman, Alex Sanchez-Stern, Bretton Chen, Sorin Lerner 等OOPSLA 2022 · 被引用 8 次
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