# MatchFixAgent: Language-Agnostic Autonomous Repository-Level Code Translation Validation and Repair

arXiv: https://arxiv.org/abs/2509.16187 · HTML: https://mlsanity.com/paper/2509.16187/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 85 references that are in the map.
- First posted on arXiv 19 Sep 2025.

## Builds on (most cited first, 30 of 30)

- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering](https://mlsanity.com/paper/2405.15793/) (2024)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [OpenHands: An Open Platform for AI Software Developers as Generalist Agents](https://mlsanity.com/paper/2407.16741/) (2024)
- [AutoCodeRover: Autonomous Program Improvement](https://mlsanity.com/paper/2404.05427/) (2024)
- [Unsupervised Translation of Programming Languages](https://mlsanity.com/paper/2006.03511/) (2020)
- [SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution](https://mlsanity.com/paper/2502.18449/) (2025)
- [RepairAgent: An Autonomous, LLM-Based Agent for Program Repair](https://mlsanity.com/paper/2403.17134/) (2024)
- [SWE-bench Multimodal: Do AI Systems Generalize to Visual Software Domains?](https://mlsanity.com/paper/2410.03859/) (2024)
- [Large Language Model-Based Agents for Software Engineering: A Survey](https://mlsanity.com/paper/2409.02977/) (2024)
- [RepoGraph: Enhancing AI Software Engineering with Repository-level Code Graph](https://mlsanity.com/paper/2410.14684/) (2024)
- [SpecRover: Code Intent Extraction via LLMs](https://mlsanity.com/paper/2408.02232/) (2024)
- [Exploring and Unleashing the Power of Large Language Models in Automated Code Translation](https://mlsanity.com/paper/2404.14646/) (2024)
- [Towards Translating Real-World Code with LLMs: A Study of Translating to Rust](https://mlsanity.com/paper/2405.11514/) (2024)
- [Evaluating Fuzz Testing](https://mlsanity.com/paper/1808.09700/) (2018)
- [AlphaTrans: A Neuro-Symbolic Compositional Approach for Repository-Level Code Translation and Validation](https://mlsanity.com/paper/2410.24117/) (2024)
- [EquiBench: Benchmarking Large Language Models' Reasoning about Program Semantics via Equivalence Checking](https://mlsanity.com/paper/2502.12466/) (2025)
- [Do Developers Update Their Library Dependencies? An Empirical Study on the Impact of Security Advisories on Library Migration](https://mlsanity.com/paper/1709.04621/) (2017)
- [VERT: Verified Equivalent Rust Transpilation with Large Language Models as Few-Shot Learners](https://mlsanity.com/paper/2404.18852/) (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)
- [Syzygy: Dual Code-Test C to (safe) Rust Translation using LLMs and Dynamic Analysis](https://mlsanity.com/paper/2412.14234/) (2024)
- [What can Large Language Models Capture about Code Functional Equivalence?](https://mlsanity.com/paper/2408.11081/) (2024)
- [Migrating Code At Scale With LLMs At Google](https://mlsanity.com/paper/2504.09691/) (2025)
- [An Empirical Study of Usages, Updates and Risks of Third-Party Libraries in Java Projects](https://mlsanity.com/paper/2002.11028/) (2020)
- [Scalable, Validated Code Translation of Entire Projects using Large Language Models](https://mlsanity.com/paper/2412.08035/) (2024)
- [RustMap: Towards Project-Scale C-to-Rust Migration via Program Analysis and LLM](https://mlsanity.com/paper/2503.17741/) (2025)
- [ClassEval-T: Evaluating Large Language Models in Class-Level Code Translation](https://mlsanity.com/paper/2411.06145/) (2024)
- [RustRepoTrans: Repository-level Code Translation Benchmark Targeting Rust](https://mlsanity.com/paper/2411.13990/) (2024)
- [Program Skeletons for Automated Program Translation](https://mlsanity.com/paper/2504.07483/) (2025)

## Built on by (most cited first, 4 of 4)

- [Adversarial Agent Collaboration for Correctness Improvements of C to Safe Rust Translation](https://mlsanity.com/paper/2510.03879/) (2025)
- [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)
- [ORBIT: Guided Agentic Orchestration for Autonomous C-to-Rust Transpilation](https://mlsanity.com/paper/2604.12048/) (2026)

## Abstract (arXiv)

Code translation transforms source code from one programming language (PL) to another. Validating the functional equivalence of translation and repairing, if necessary, are critical steps in code translation. Existing automated validation and repair approaches struggle to generalize to many PLs due to high engineering overhead, and they rely on existing and often inadequate test suites, which results in false claims of equivalence and ineffective translation repair. To bridge this gap, we develop MatchFixAgent, a large language model (LLM)-based, PL-agnostic framework for equivalence validation and repair of translations. MatchFixAgent features a multi-agent architecture that divides equivalence validation into several sub-tasks to ensure thorough and consistent semantic analysis of the translation. We compare MatchFixAgent's validation and repair results with four repository-level code translation techniques. Our results demonstrate that MatchFixAgent produces (in)equivalence verdicts for 99.2% of translation pairs, with the same equivalence validation result as prior work on 72.8% of them. When MatchFixAgent's result disagrees with prior work, we find that 60.7% of the time MatchFixAgent's result is actually correct. In addition, we show that MatchFixAgent can repair 50.6% of inequivalent translation, compared to prior work's 18.5%.