Automated Proof Generation for Rust Code via Self-Evolution

Tianyu Chen, Shuai Lu, Shan Lu, Yeyun Gong, Chenyuan Yang, Xuheng Li, Md Rakib Hossain Misu, Hao Yu, Nan Duan, Peng CHENG, Fan Yang, Shuvendu K Lahiri, Tao Xie, Lidong Zhou

Keywords: Large Language Models, Program Verification

Abstract:

Ensuring correctness is crucial for code generation. Formal verification offers a
definitive assurance of correctness, but demands substantial human effort in proof
construction and hence raises a pressing need for automation. The primary obsta-
cle lies in the severe lack of data—there is much fewer proofs than code snippets
for Large Language Models (LLMs) to train upon. In this paper, we introduce
SAFE, a framework that overcomes the lack of human-written proofs to enable
automated proof generation of Rust code. SAFE establishes a self-evolving cycle
where data synthesis and fine-tuning collaborate to enhance the model capability,
leveraging the definitive power of a symbolic verifier in telling correct proofs from
incorrect ones. SAFE also re-purposes the large number of synthesized incorrect
proofs to train the self-debugging capability of the fine-tuned models, empowering
them to fix incorrect proofs based on the verifier’s feedback. SAFE demonstrates
superior efficiency and precision compared to GPT-4o. Through tens of thousands
of synthesized proofs and the self-debugging mechanism, we improve the capa-
bility of open-source models, initially unacquainted with formal verification, to
automatically write proofs for Rust code. This advancement leads to a signifi-
cant improvement in performance, achieving a 52.52% accuracy rate in a bench-
mark crafted by human experts, a significant leap over GPT-4o’s performance of
14.39%.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 2209