Large Language Model-Powered Agent for C to Rust Code Translation

• URL: http://arxiv.org/abs/2505.15858v2
• Date: Thu, 26 Jun 2025 15:16:53 GMT
• Title: Large Language Model-Powered Agent for C to Rust Code Translation
• Authors: HoHyun Sim, Hyeonjoong Cho, Yeonghyeon Go, Zhoulai Fu, Ali Shokri, Binoy Ravindran,
• Abstract summary: A modern system programming language, Rust, has emerged as a memory-safe alternative to the C programming language.<n>Applying the agentic capability for the C-to-Rust translation introduces distinct challenges.<n>Unlike math or commonsense QA, the intermediate steps required for C-to-Rust are not well-defined.<n>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)
• Score: 2.182572303351317
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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