Authors: Zhen Yang, Fang Liu, Zhongxing Yu, Jacky Wai Keung, Jia Li, Shuo Liu, Yifan Hong, Xiaoxue Ma, Zhi Jin, Ge Li

Published on: April 23, 2024

Impact Score: 7.8

Arxiv code: Arxiv:2404.14646

Summary

• What is new: Introduction of UniTrans, a novel Unified code Translation framework that significantly improves code translation accuracy by leveraging test cases and iterative repairs.
• Why this is important: Existing learning-based code translation tools are unsatisfactory for practical use due to high resource costs and lower performance.
• What the research proposes: UniTrans framework, which uses auto-generated test cases to augment and iteratively repair code translations, improving accuracy and applicability across various LLMs.
• Results: Substantial improvements in automated code translation tasks across Python, Java, and C++ datasets, outperforming existing learning-based transpilers.

Technical Details

Technological frameworks used: UniTrans

Models used: LLMs of diverse sizes

Data used: Python, Java, C++ translation datasets

Potential Impact

Software development and translation tool markets, particularly companies in IDEs, code review, maintenance, and automated testing services.

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