Path: csiph.com!eternal-september.org!feeder3.eternal-september.org!news.iecc.com!.POSTED.news.iecc.com!nerds-end From: John R Levine Newsgroups: comp.compilers Subject: Paper: Large Language Model-Powered Agent for C to Rust Code Translation Date: Fri, 23 May 2025 13:12:38 -0400 Organization: Compilers Central Sender: johnl%iecc.com Approved: comp.compilers@iecc.com Message-ID: <25-05-015@comp.compilers> MIME-Version: 1.0 Content-Type: text/plain; charset="UTF-8" Injection-Info: gal.iecc.com; posting-host="news.iecc.com:2001:470:1f07:1126:0:676f:7373:6970"; logging-data="32668"; mail-complaints-to="abuse@iecc.com" Keywords: C, Rust, translator Posted-Date: 23 May 2025 13:13:03 EDT X-submission-address: compilers@iecc.com X-moderator-address: compilers-request@iecc.com X-FAQ-and-archives: http://compilers.iecc.com Xref: csiph.com comp.compilers:3658 Another paper claims their LLM with feedback works pretty well. Abstract The C programming language has been foundational in building system-level software. However, its manual memory management model frequently leads to memory safety issues. In response, a modern system programming language, Rust, has emerged as a memory-safe alternative. Moreover, automating the C-to-Rust translation empowered by the rapid advancements of the generative capabilities of LLMs is gaining growing interest for large volumes of legacy C code. Despite some success, existing LLM-based approaches have constrained the role of LLMs to static prompt-response behavior and have not explored their agentic problem-solving capability. Applying the LLM agentic capability for the C-to-Rust translation introduces distinct challenges, as this task differs from the traditional LLM agent applications, such as math or commonsense QA domains. First, the scarcity of parallel C-to-Rust datasets hinders the retrieval of suitable code translation exemplars for in-context learning. Second, unlike math or commonsense QA, the intermediate steps required for C-to-Rust are not well-defined. Third, it remains unclear how to organize and cascade these intermediate steps to construct a correct translation trajectory. To address these challenges in the C-to-Rust translation, we propose a novel intermediate step, the Virtual Fuzzing-based equivalence Test (VFT), and an agentic planning framework, the LLM-powered Agent for C-to-Rust code translation (LAC2R). The VFT guides LLMs to identify input arguments that induce divergent behaviors between an original C function and its Rust counterpart and to generate informative diagnoses to refine the unsafe Rust code. LAC2R uses the MCTS to systematically organize the LLM-induced intermediate steps for correct translation. We experimentally demonstrated that LAC2R effectively conducts C-to-Rust translation on large-scale, real-world benchmarks. https://arxiv.org/abs/2505.15858 Regards, John Levine, johnl@taugh.com, Taughannock Networks, Trumansburg NY Please consider the environment before reading this e-mail. https://jl.ly