# C2RustXW: Program-Structure-Aware C-to-Rust Translation via Program Analysis and LLM

arXiv: https://arxiv.org/abs/2603.28686 · HTML: https://mlsanity.com/paper/2603.28686/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2026.

## Built on by (most cited first, 1 of 1)

- [Translator vs. Challenger: Adversarial Agentic Learning for C-to-Rust Translation](https://mlsanity.com/paper/2609.15381/) (2026)

## Abstract (arXiv)

The growing adoption of Rust for its memory safety and performance has increased the demand for effective migration of legacy C codebases. However, existing rule-based translators (e.g., ctorust) often generate verbose, non-idiomatic code that preserves unsafe C semantics, limiting readability, maintainability, and practical adoption. Moreover, manual post-processing of such outputs is labor-intensive and rarely yields high-quality Rust code, posing a significant barrier to large-scale migration. To address these limitations, we present tool, a program-structure-aware C-to-Rust translation approach that integrates program analysis with Large Language Models (LLMs). tool extracts the multi-level program structure, including global symbols, function dependencies, and control- and data-flow information, and encodes these as structured textual representations injected into LLM prompts to guide translation and repair. Based on this design, tool performs dependency-aware translation and adopts a multi-stage repair pipeline that combines rule-based and structure-guided LLM-based techniques to ensure syntactic correctness. For semantic correctness, tool further integrates execution-based validation with structure-guided reasoning to localize and repair behavioral inconsistencies. Experimental results show that tool achieves 100% syntactic correctness on CodeNet and 97.78% on GitHub, while significantly reducing code size (up to 43.70%) and unsafe usage (to 5.75%). At the project level, tool achieves perfect syntactic correctness and an average semantic correctness of 78.87%, demonstrating its effectiveness for practical and scalable C-to-Rust migration.