SmartC2Rust: Iterative, Feedback-Driven C-to-Rust Translation via Large Language Models for Safety and Equivalence

Abstract

Memory safety vulnerabilities remain prevalent in today’s software systems and one promising solution to mitigate them is to adopt memory-safe languages such as Rust. Due to legacy code written in memory unsafe C, there is strong motivation to translate legacy C code into Rust. Prior works have already shown promise in using Large Language Models (LLMs) for such translations. However, significant challenges persist for LLM-based translation: the translated code often fails to compile, let alone reduce unsafe statements and maintain the semantic functionalities due to inherent limitations of LLMs such as limited token size and inconsistent outputs. In this paper, we design an automated C-to-Rust translation system, called SmartC2Rust, to segment and convert the C code to Rust with memory safety and semantic equivalence. The key insight is to iteratively refine the output Rust code with additional feedback, e.g., compilation errors, segmentation contexts, semantic discrepancies, and memory unsafe statements. Such feedback will gradually improve the quality of generated Rust code, thus mitigating unsafety, inconsistency, and semantic issues. Our evaluation shows that SmartC2Rust significantly decreases the unsafe statements and outperforms prior works in security and semantic equivalence.

Reference

Momoko Shiraishi, Yinzhi Cao, Takahiro Shinagawa. SmartC2Rust: Iterative, Feedback-Driven C-to-Rust Translation via Large Language Models for Safety and Equivalence. In Proceedings of the 48th IEEE/ACM International Conference on Software Engineering (ICSE 2026), Apr, 2026. Acceptance Ratio: 24.2% (160/660).