# Towards Translating Real-World Code with LLMs: A Study of Translating to Rust

arXiv: https://arxiv.org/abs/2405.11514 · HTML: https://mlsanity.com/paper/2405.11514/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2024.

## Built on by (most cited first, 30 of 35)

- [A Survey on Large Language Models for Code Generation](https://mlsanity.com/paper/2406.00515/) (2024)
- [CITER: Collaborative Inference for Efficient Large Language Model Decoding with Token-Level Routing](https://mlsanity.com/paper/2502.01976/) (2025)
- [Large Language Models for Code Generation: A Comprehensive Survey of Challenges, Techniques, Evaluation, and Applications](https://mlsanity.com/paper/2503.01245/) (2025)
- [RepoTransBench: A Real-World Multilingual Benchmark for Repository-Level Code Translation](https://mlsanity.com/paper/2412.17744/) (2024)
- [CRUST-Bench: A Comprehensive Benchmark for C-to-safe-Rust Transpilation](https://mlsanity.com/paper/2504.15254/) (2025)
- [EvoC2Rust: A Skeleton-guided Framework for Project-Level C-to-Rust Translation](https://mlsanity.com/paper/2508.04295/) (2025)
- [Syzygy: Dual Code-Test C to (safe) Rust Translation using LLMs and Dynamic Analysis](https://mlsanity.com/paper/2412.14234/) (2024)
- [From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence](https://mlsanity.com/paper/2511.18538/) (2025)
- [Migrating Code At Scale With LLMs At Google](https://mlsanity.com/paper/2504.09691/) (2025)
- [CODEMENV: Benchmarking Large Language Models on Code Migration](https://mlsanity.com/paper/2506.00894/) (2025)
- [SACTOR: LLM-Driven Correct and Idiomatic C to Rust Translation with Static Analysis and FFI-Based Verification](https://mlsanity.com/paper/2503.12511/) (2025)
- [Human oversight of agentic systems in practice: Examining the oversight work, challenges, and heuristics of developers using software agents](https://mlsanity.com/paper/2606.05391/) (2026)
- [Meta-RAG on Large Codebases Using Code Summarization](https://mlsanity.com/paper/2508.02611/) (2025)
- [ALMAS: an Autonomous LLM-based Multi-Agent Software Engineering Framework](https://mlsanity.com/paper/2510.03463/) (2025)
- [EffiReasonTrans: RL-Optimized Reasoning for Code Translation](https://mlsanity.com/paper/2510.18863/) (2025)
- [EditLord: Learning Code Transformation Rules for Code Editing](https://mlsanity.com/paper/2504.15284/) (2025)
- [MatchFixAgent: Language-Agnostic Autonomous Repository-Level Code Translation Validation and Repair](https://mlsanity.com/paper/2509.16187/) (2025)
- [Program Skeletons for Automated Program Translation](https://mlsanity.com/paper/2504.07483/) (2025)
- [Adversarial Agent Collaboration for Correctness Improvements of C to Safe Rust Translation](https://mlsanity.com/paper/2510.03879/) (2025)
- [Project-Level C-to-Rust Translation via Pointer Knowledge Graphs](https://mlsanity.com/paper/2510.10956/) (2025)
- [Can LLMs Replace Humans During Code Chunking?](https://mlsanity.com/paper/2506.19897/) (2025)
- [ENCRUST: Encapsulated Substitution and Agentic Refinement on a Live Scaffold for Safe C-to-Rust Translation](https://mlsanity.com/paper/2604.04527/) (2026)
- [Autonomous Legacy Web Application Upgrades Using a Multi-Agent System](https://mlsanity.com/paper/2501.19204/) (2025)
- [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)
- [A Pilot Study on LLM-Based Agentic Translation from Android to iOS: Pitfalls and Insights](https://mlsanity.com/paper/2507.16037/) (2025)
- [Using Contrastive Learning to Improve Two-Way Reasoning in Large Language Models: The Obfuscation Task as a Case Study](https://mlsanity.com/paper/2509.05553/) (2025)
- [Feedback Loops and Code Perturbations in LLM-based Software Engineering: A Case Study on a C-to-Rust Translation System](https://mlsanity.com/paper/2512.02567/) (2025)
- [SoK: Understanding (New) Security Issues Across AI4Code Use Cases](https://mlsanity.com/paper/2512.18456/) (2025)
- [Revisiting the Role of Natural Language Code Comments in Code Translation](https://mlsanity.com/paper/2601.16661/) (2026)

## Abstract (arXiv)

Large language models (LLMs) show promise in code translation - the task of translating code written in one programming language to another language - due to their ability to write code in most programming languages. However, LLM's effectiveness on translating real-world code remains largely unstudied. In this work, we perform the first substantial study on LLM-based translation to Rust by assessing the ability of five state-of-the-art LLMs, GPT4, Claude 3, Claude 2.1, Gemini Pro, and Mixtral. We conduct our study on code extracted from real-world open source projects. To enable our study, we develop FLOURINE, an end-to-end code translation tool that uses differential fuzzing to check if a Rust translation is I/O equivalent to the original source program, eliminating the need for pre-existing test cases. As part of our investigation, we assess both the LLM's ability to produce an initially successful translation, as well as their capacity to fix a previously generated buggy one. If the original and the translated programs are not I/O equivalent, we apply a set of automated feedback strategies, including feedback to the LLM with counterexamples. Our results show that the most successful LLM can translate 47% of our benchmarks, and also provides insights into next steps for improvements.