Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
7-2025
Abstract
Migrating existing C programs into Rust is increasingly desired, as Rust offers superior memory safety while maintaining C’s high performance. 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’s capabilities to produce idiomatic Rust code from given small pieces of C code, (2) Mitigate LLM’s incapability in handling large codebases by breaking project-scale C programs into smaller units for translation according to their usage dependencies, and (3) Enhance the correctness of the translated Rust program by utilizing available test cases to check input/output equivalence between C and Rust code, and iteratively utilizing the feedback from compilation and testing errors for LLM to refine translated Rust code. We have empirically evaluated RustMap on 126 sample real-world programs, including 125 programs from Rosetta Code and a complex bzip2 program containing more than 7000 lines of code, using GPT-4o as the LLM. RustMap shows promising results in guiding GPT-4o to translate most of the C code into more idiomatic, readable, and functional Rust code with significantly less unsafe code than other translation tools, presenting non-trivial translation patterns that may be reusable for future research.
Discipline
Artificial Intelligence and Robotics
Research Areas
Intelligent Systems and Optimization
Areas of Excellence
Digital transformation
Publication
Engineering of Complex Computer Systems: 29th International Conference, ICECCS 2025, Hangzhou, China, July 2-4
First Page
283
Last Page
302
Identifier
10.1007/978-3-032-00828-2_16
Publisher
ACM
City or Country
New York
Citation
CAI, Xuemeng; LIU, Jiakun; HUANG, Xiping; YU, Yijun; WU, Haitao; LI, Chunmiao; WANG, Bo; IMAM NUR BANI YUSUF; and JIANG, Lingxiao.
RustMap: Towards project-scale C-to-Rust migration via program analysis and LLM. (2025). Engineering of Complex Computer Systems: 29th International Conference, ICECCS 2025, Hangzhou, China, July 2-4. 283-302.
Available at: https://ink.library.smu.edu.sg/sis_research/10765
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.