Search-Based Multi-Trajectory Refinement for Safe C-to-Rust Translation with Large Language Models

TL;DR

This paper introduces LAC2R, a systematic search-based method using MCTS to improve C-to-Rust translation with LLMs, achieving high safety and correctness in real-world benchmarks.

Contribution

It presents a novel MCTS-guided refinement approach for C-to-Rust translation that effectively explores multiple translation trajectories and intermediate steps.

Findings

LAC2R achieves the highest safety ratio and project correctness on small benchmarks.

LAC2R outperforms existing methods in safety and correctness metrics.

The approach effectively handles large-scale real-world C code.

Abstract

The C programming language has been foundational in building system-level software. However, its manual memory management model frequently leads to memory safety issues. In response, Rust has emerged as a memory-safe alternative. Moreover, automating the C-to-Rust translation empowered by the rapid advancements of the generative capabilities of LLMs is gaining growing interest for large volumes of legacy C code. Leveraging LLM for the C-to-Rust translation introduces distinct challenges, unlike the math or commonsense QA domains where the LLMs have been predominantly applied. First, the scarcity of parallel C-to-Rust datasets hinders the retrieval of suitable code translation exemplars for in-context learning. Second, unlike math or commonsense QA problems, the intermediate steps required for C-to-Rust are not well-defined. Third, it remains unclear how to organize and cascade these intermediate steps to construct a correct translation trajectory. While existing LLM-based approaches have achieved some success, they have relied on iterative code refinement along a single search trajectory on a C-to-Rust problem space and have not explored the use of systematic search mechanisms to navigate the space of possible refinement trajectories. To address these challenges in the C-to-Rust translation, we propose the MCTS-Guided LLM refinement technique for automated C-to-safe-Rust translation (LAC2R). LAC2R uses MCTS to systematically explore multiple refinement trajectories and organize the LLM-induced intermediate steps for correct translation. We experimentally demonstrated that LAC2R effectively conducts C-to-Rust translation on large-scale, real-world benchmarks. On small-scale benchmarks, LAC2R is the only method that simultaneously attains the highest safety ratio, perfect project-level correctness, and the fewest linter warnings among the compared methods.