[2409.19894] TransAgent: Enhancing LLM-Based Code Translation via Fine-Grained Execution Alignment

Computer Science > Software Engineering arXiv:2409.19894 (cs) [Submitted on 30 Sep 2024 (v1), last revised 7 Apr 2026 (this version, v5)] Title:TransAgent: Enhancing LLM-Based Code Translation via Fine-Grained Execution Alignment Authors:Zhiqiang Yuan, Weitong Chen, Hanlin Wang, Xin Peng, Zhenpeng Chen, Yiling Lou View a PDF of the paper titled TransAgent: Enhancing LLM-Based Code Translation via Fine-Grained Execution Alignment, by Zhiqiang Yuan and 5 other authors View PDF HTML (experimental) Abstract:Code translation transforms code between programming languages while preserving functionality, which is critical in software development and maintenance. While traditional learning-based code translation methods have limited effectiveness due to the lack of sufficient parallel training data, Large Language Models (LLMs) have recently advanced this field with their strong code generation and comprehension capabilities. However, code translated by LLMs still suffers from diverse quality issues, such as syntax and semantic errors. In this work, we propose TransAGENT, a novel multi-agent system that eliminates the errors during LLM-based code translation. The main insight of TransAGENT is to localize error-prone code blocks via fine-grained execution alignment between source and target code. We evaluate TransAGENT on a newly constructed benchmark of recent programming tasks to mitigate data leakage. TransAGENT outperforms the latest UniTrans by up to 33.3% in translation accura...