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Reducing the Costs of Proof Synthesis on Rust Systems by Scaling Up a Seed Training Set
Nongyu Di, Tianyu Chen, Shan Lu +6
Large Language Models (LLMs) are widely used for code generation. However, the correctness of code generated by LLMs remains a concern. A potential remedy to this concern is to hav…
Automated Proof Generation for Rust Code via Self-Evolution
Tianyu Chen, Shuai Lu, Shan Lu +11
Ensuring correctness is crucial for code generation. Formal verification offers a definitive assurance of correctness, but demands substantial human effort in proof construction an…
AutoVerus: Automated Proof Generation for Rust Code
Chenyuan Yang, Xuheng Li, Md Rakib Hossain Misu +10
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 generatio…
What's in a Proof? Analyzing Expert Proof-Writing Processes in F* and Verus
Rijul Jain, Shraddha Barke, Gabriel Ebner +3
Proof-oriented programming languages (POPLs) empower developers to write code alongside formal correctness proofs, providing formal guarantees that the code adheres to specified re…