Formal verification of Rust programs with Verus provides strong correctness guarantees, but developing the auxiliary specifications and proofs still requires considerable manual effort. Existing LLM-based proof-synthesis agents can automate part of this process, yet their effectiveness on non-trivial tasks is often lim...
Yu-Cheng Zhang, Cheng Wen, Jia-Lun Cao et al.· Proceedings of the 41st IEEE...· 0 citations
Rust's ownership and type system provide strong memory safety guarantees, but unsafe code still presents memory safety risks. Formal verification is crucial for ensuring memory safety, but writing precise specifications for unsafe Rust is challenging and largely manual. Large language models (LLMs) have shown promise i...
A multi-agent collaborative framework, StarVerus, to automate the verification of industrial Rust code and introduces a planner-repairer-actor-rewriter multi-agent paradigm to further enhance the proof repair capabilities.
Chao Jiang, Ding Wang, Du-Gang Liu et al.· Proceedings of the 32nd ACM...· 0 citations
This paper presents HarnessLLM, an automated workflow that leverages LLMs to generate verification harnesses for Rust code directly from existing test suites and is the first work to use LLMs for generating harnesses aimed at memory safety verification in real-world Rust projects.
Minghua Wang, Yuwei Liu, Lin Huang· Proceedings of the 2026 IEEE...· 0 citations
Creating code specifications is a crucial measure to improve the trustworthiness of many industrial systems implemented in Rust with high security requirements. Because writing specifications requires highly specialized professionals and is time-consuming, the automatic generation of specifications, enabled by large la...
Chao Jiang, Ding Wang, Dugang Liu et al.· Proceedings of the 32nd ACM...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
