AutoVerus: Automated Proof Generation for Rust Code

• URL: http://arxiv.org/abs/2409.13082v1
• Date: Thu, 19 Sep 2024 20:40:52 GMT
• Title: AutoVerus: Automated Proof Generation for Rust Code
• Authors: Chenyuan Yang, Xuheng Li, Md Rakib Hossain Misu, Jianan Yao, Weidong Cui, Yeyun Gong, Chris Hawblitzel, Shuvendu Lahiri, Jacob R. Lorch, Shuai Lu, Fan Yang, Ziqiao Zhou, Shan Lu,
• Abstract summary: AutoVerus uses large language model (LLM) to automatically generate correctness proof for Rust code. Our evaluation shows that AutoVerus can automatically generate correct proof for more than 90% of them.
• Score: 25.56534570237484
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative AI has shown its values for many software engineering tasks. Still in its infancy, large language model (LLM)-based proof generation lags behind LLM-based code generation. In this paper, we present AutoVerus. AutoVerus uses LLM to automatically generate correctness proof for Rust code. AutoVerus is designed to match the unique features of Verus, a verification tool that can prove the correctness of Rust code using proofs and specifications also written in Rust. AutoVerus consists of a network of LLM agents that are crafted and orchestrated to mimic human experts' three phases of proof construction: preliminary proof generation, proof refinement guided by generic tips, and proof debugging guided by verification errors. To thoroughly evaluate AutoVerus and help foster future research in this direction, we have built a benchmark suite of 150 non-trivial proof tasks, based on existing code-generation benchmarks and verification benchmarks. Our evaluation shows that AutoVerus can automatically generate correct proof for more than 90% of them, with more than half of them tackled in less than 30 seconds or 3 LLM calls.

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