AutoVerus: Automated Proof Generation for Rust Code

Generative AI has shown its value 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 LLMs 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 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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Publication Details

Journal
Proceedings of the ACM on Programming Languages
Published
2025-10-09
DOI
https://doi.org/10.1145/3763174
Citations
3
Primary Topic
Algorithms and Data Compression
Type
article
Field-Weighted Citation Impact
7.44
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article

AutoVerus: Automated Proof Generation for Rust Code

Md Rakib Hossain Misu, Chris Hawblitzel, Ziqiao Zhou, Jianan Yao et al.
3 citations
Proceedings of the ACM on Programming Languages
Algorithms and Data Compression
7.44
article

AutoVerus: Automated Proof Generation for Rust Code

Md Rakib Hossain Misu, Chris Hawblitzel, Ziqiao Zhou, Jianan Yao, Shuvendu K. Lahiri, Yeyun Gong, Jacob R. Lorch, Shan Lu, Chenyuan Yang, Fan Yang, X. Li, Weidong Cui, Shuai Lu
article en
3 citations

Abstract

Generative AI has shown its value 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 LLMs 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 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.

Proceedings of the ACM on Programming LanguagesVol. 9(OOPSLA2)
Microsoft (United States) (US), University of Illinois Urbana-Champaign (US), University of Toronto (CA), University of California, Irvine (US), University of Chicago (US), Microsoft Research Asia (China) (CN), Columbia University (US)
Openalex Percentile: Top 3%
Algorithms and Data Compression
7.44
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