# EvoC2Rust: A Skeleton-guided Framework for Project-Level C-to-Rust Translation

arXiv: https://arxiv.org/abs/2508.04295 · HTML: https://mlsanity.com/paper/2508.04295/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 57 references that are in the map.
- First posted on arXiv 6 Aug 2025.

## Builds on (most cited first, 20 of 20)

- [M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation](https://mlsanity.com/paper/2402.03216/) (2024)
- [Executable Code Actions Elicit Better LLM Agents](https://mlsanity.com/paper/2402.01030/) (2024)
- [Unsupervised Translation of Programming Languages](https://mlsanity.com/paper/2006.03511/) (2020)
- [Productivity Assessment of Neural Code Completion](https://mlsanity.com/paper/2205.06537/) (2022)
- [Exploring and Unleashing the Power of Large Language Models in Automated Code Translation](https://mlsanity.com/paper/2404.14646/) (2024)
- [From Code to Correctness: Closing the Last Mile of Code Generation with Hierarchical Debugging](https://mlsanity.com/paper/2410.01215/) (2024)
- [Explainable Automated Debugging via Large Language Model-driven Scientific Debugging](https://mlsanity.com/paper/2304.02195/) (2023)
- [Towards Translating Real-World Code with LLMs: A Study of Translating to Rust](https://mlsanity.com/paper/2405.11514/) (2024)
- [CRUST-Bench: A Comprehensive Benchmark for C-to-safe-Rust Transpilation](https://mlsanity.com/paper/2504.15254/) (2025)
- [C2SaferRust: Transforming C Projects into Safer Rust with NeuroSymbolic Techniques](https://mlsanity.com/paper/2501.14257/) (2025)
- [Unraveling the Potential of Large Language Models in Code Translation: How Far Are We?](https://mlsanity.com/paper/2410.09812/) (2024)
- [Syzygy: Dual Code-Test C to (safe) Rust Translation using LLMs and Dynamic Analysis](https://mlsanity.com/paper/2412.14234/) (2024)
- [Ownership guided C to Rust translation](https://mlsanity.com/paper/2303.10515/) (2023)
- [RustMap: Towards Project-Scale C-to-Rust Migration via Program Analysis and LLM](https://mlsanity.com/paper/2503.17741/) (2025)
- [To Tag, or Not to Tag: Translating C's Unions to Rust's Tagged Unions](https://mlsanity.com/paper/2408.11418/) (2024)
- [Translating C To Rust: Lessons from a User Study](https://mlsanity.com/paper/2411.14174/) (2024)
- [SafeTrans: LLM-assisted Transpilation from C to Rust](https://mlsanity.com/paper/2505.10708/) (2025)
- [Concrat: An Automatic C-to-Rust Lock API Translator for Concurrent Programs](https://mlsanity.com/paper/2301.10943/) (2023)
- [Forcrat: Automatic I/O API Translation from C to Rust via Origin and Capability Analysis](https://mlsanity.com/paper/2506.01427/) (2025)
- [APIRAT: Integrating Multi-source API Knowledge for Enhanced Code Translation with LLMs](https://mlsanity.com/paper/2504.14852/) (2025)

## Built on by (most cited first, 19 of 19)

- [LongCodeZip: Compress Long Context for Code Language Models](https://mlsanity.com/paper/2510.00446/) (2025)
- [SWE-QA: Can Language Models Answer Repository-level Code Questions?](https://mlsanity.com/paper/2509.14635/) (2025)
- [Reasoning in Trees: Improving Retrieval-Augmented Generation for Multi-Hop Question Answering](https://mlsanity.com/paper/2601.11255/) (2026)
- [Seeing is Coding: On the Effectiveness of Vision Language Models in Code Understanding](https://mlsanity.com/paper/2602.01785/) (2026)
- [EffiSkill: Agent Skill Based Automated Code Efficiency Optimization](https://mlsanity.com/paper/2603.27850/) (2026)
- [Neuron-Guided Interpretation of Code LLMs: Where, Why, and How?](https://mlsanity.com/paper/2512.19980/) (2025)
- [Adversarial Agent Collaboration for Correctness Improvements of C to Safe Rust Translation](https://mlsanity.com/paper/2510.03879/) (2025)
- [HyperAgent: Leveraging Hypergraphs for Topology Optimization in Multi-Agent Communication](https://mlsanity.com/paper/2510.10611/) (2025)
- [ENCRUST: Encapsulated Substitution and Agentic Refinement on a Live Scaffold for Safe C-to-Rust Translation](https://mlsanity.com/paper/2604.04527/) (2026)
- [ReCodeAgent: A Multi-agent Workflow for Language-Agnostic Translation and Validation of Large-Scale Repositories](https://mlsanity.com/paper/2604.07341/) (2026)
- [Large Language Models for Multilingual Code Intelligence: A Survey](https://mlsanity.com/paper/2604.25960/) (2026)
- [ClassEval-Pro: A Cross-Domain Benchmark for Class-Level Code Generation](https://mlsanity.com/paper/2604.26923/) (2026)
- [His2Trans: A Knowledge-Guided Agentic Framework for Project-Level C-to-Rust Migration](https://mlsanity.com/paper/2603.02617/) (2026)
- [ORBIT: Guided Agentic Orchestration for Autonomous C-to-Rust Transpilation](https://mlsanity.com/paper/2604.12048/) (2026)
- [SAMAS: A Spectrum-Guided Multi-Agent System for Achieving Style Fidelity in Literary Translation](https://mlsanity.com/paper/2602.19840/) (2026)
- [Documentation-Guided Agentic Codebase Migration from C to Rust](https://mlsanity.com/paper/2605.14634/) (2026)
- [Fine-Tuning Qwen3-27B for C-to-Rust Code Translation: A Three-Stage Curriculum of Pretraining, Debugging-Aware SFT, and Task-Specific SFT](https://mlsanity.com/paper/2608.13681/) (2026)
- [DeepRepoQA: Code Repository Question Answering with Deep Agent Exploration](https://mlsanity.com/paper/2608.24221/) (2026)
- [Translator vs. Challenger: Adversarial Agentic Learning for C-to-Rust Translation](https://mlsanity.com/paper/2609.15381/) (2026)

## Abstract (arXiv)

Translating legacy C codebases to Rust is increasingly demanded for building safety-critical systems. While various approaches have emerged for this task, they face inherent trade-offs: rule-based methods often struggle to satisfy code safety and idiomaticity requirements, while LLM-based methods frequently fail to generate semantically equivalent Rust code, due to the heavy dependencies of modules across the entire codebase. Recent studies have revealed that both solutions are limited to small-scale programs. In this paper, we propose EvoC2Rust, an automated framework for converting complete C projects to equivalent Rust ones. EvoC2Rust employs a skeleton-guided translation strategy for project-level translation. The pipeline consists of three stages: 1) it first decomposes the C project into functional modules, employs a feature-mapping-enhanced LLM to transform definitions and macros, and generates type-checked function stubs, which form a compilable Rust skeleton; 2) it then incrementally translates functions, replacing the corresponding stub placeholders; 3) finally, it repairs compilation errors by integrating LLM and static analysis. Through evolutionary augmentation, EvoC2Rust combines the advantages of both rule-based and LLM-based solutions. Our evaluation on open-source benchmarks and six industrial projects demonstrates the superior performance of EvoC2Rust in project-level C-to-Rust translation. The results show that our approach outperforms the strongest LLM-based baseline by 17.24% in syntax accuracy and 14.32% in semantic accuracy, while also achieving a 43.59% higher code safety rate than the best rule-based tool.