RepoTransBench: A Real-World Benchmark for Repository-Level Code Translation

• URL: http://arxiv.org/abs/2412.17744v1
• Date: Mon, 23 Dec 2024 17:52:10 GMT
• Title: RepoTransBench: A Real-World Benchmark for Repository-Level Code Translation
• Authors: Yanli Wang, Yanlin Wang, Suiquan Wang, Daya Guo, Jiachi Chen, John Grundy, Xilin Liu, Yuchi Ma, Mingzhi Mao, Hongyu Zhang, Zibin Zheng,
• Abstract summary: Repository-level code translation refers to translating an entire code repository from one programming language to another. Previous benchmarks mostly provide fine-grained samples, focusing at either code snippet, function, or file-level code translation. We propose RepoTransBench, which is a real-world repository-level code translation benchmark with an automatically executable test suite.
• Score: 44.856816446807265
• License:
• Abstract: Repository-level code translation refers to translating an entire code repository from one programming language to another while preserving the functionality of the source repository. Many benchmarks have been proposed to evaluate the performance of such code translators. However, previous benchmarks mostly provide fine-grained samples, focusing at either code snippet, function, or file-level code translation. Such benchmarks do not accurately reflect real-world demands, where entire repositories often need to be translated, involving longer code length and more complex functionalities. To address this gap, we propose a new benchmark, named RepoTransBench, which is a real-world repository-level code translation benchmark with an automatically executable test suite. We conduct experiments on RepoTransBench to evaluate the translation performance of 11 advanced LLMs. We find that the Success@1 score (test success in one attempt) of the best-performing LLM is only 7.33%. To further explore the potential of LLMs for repository-level code translation, we provide LLMs with error-related feedback to perform iterative debugging and observe an average 7.09% improvement on Success@1. However, even with this improvement, the Success@1 score of the best-performing LLM is only 21%, which may not meet the need for reliable automatic repository-level code translation. Finally, we conduct a detailed error analysis and highlight current LLMs' deficiencies in repository-level code translation, which could provide a reference for further improvements.

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