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

Understand

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.