RustMap: Towards Project-Scale C-to-Rust Migration via Program Analysis and LLM

• URL: http://arxiv.org/abs/2503.17741v1
• Date: Sat, 22 Mar 2025 11:57:45 GMT
• Title: RustMap: Towards Project-Scale C-to-Rust Migration via Program Analysis and LLM
• Authors: Xuemeng Cai, Jiakun Liu, Xiping Huang, Yijun Yu, Haitao Wu, Chunmiao Li, Bo Wang, Imam Nur Bani Yusuf, Lingxiao Jiang,
• Abstract summary: Rust offers superior memory safety while maintaining C's high performance.<n>Existing automated translation tools, such as C2Rust, may rely too much on syntactic, template-based translation.<n>This paper introduces a novel dependency-guided and large language model (LLM)-based C-to-Rust translation approach, RustMap.
• Score: 13.584956125542396
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Migrating existing C programs into Rust is increasingly desired, as Rust offers superior memory safety while maintaining C's high performance. However, vastly different features between C and Rust--e.g., distinct definitions and usages of pointers and references--pose significant challenges beyond mere syntactic translation. Existing automated translation tools, such as C2Rust, may rely too much on syntactic, template-based translation and generate unsafe Rust code that is hard for human developers to read, maintain, or even compile. More semantic-aware translation that produces safer, idiomatic, and runnable Rust code is much needed. This paper introduces a novel dependency-guided and large language model (LLM)-based C-to-Rust translation approach, RustMap, based on three key ideas: (1) Utilize LLM capabilities to produce idiomatic Rust code from given small pieces of C code, (2) Mitigate LLM limitations in handling large codebases by breaking project-scale C programs into smaller units for translation according to their usage dependencies and composing them into a runnable Rust program, and (3) Enhance the correctness of the translated Rust program by using test cases to check input/output equivalence, isolate faulty code when execution states deviate, and iteratively refine the translation using feedback from compilation and test errors. We empirically evaluate RustMap on 126 real-world programs, including 125 from Rosetta Code and a 7000+ line bzip2 implementation using GPT-4o as the LLM. RustMap shows promising results, guiding GPT-4o to produce idiomatic, readable, and functional Rust code with significantly less unsafe code than other tools, and revealing non-trivial translation patterns reusable for future research.

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