Enhancing LLM-based Code Translation in Repository Context via Triple Knowledge-Augmented

• URL: http://arxiv.org/abs/2503.18305v2
• Date: Thu, 27 Mar 2025 07:16:23 GMT
• Title: Enhancing LLM-based Code Translation in Repository Context via Triple Knowledge-Augmented
• Authors: Guangsheng Ou, Mingwei Liu, Yuxuan Chen, Xueying Du, Shengbo Wang, Zekai Zhang, Xin Peng, Zibin Zheng,
• Abstract summary: Large language models (LLMs) have behaved well in function-level code translation without repository-level context.<n>We propose K-Trans, which leverages triple knowledge augmentation to enhance LLM's translation quality under repository context.<n>Experiments show that K-Trans substantially outperforms the baseline adapted from previous work by 19.4%/40.2% relative improvement in pass@1 and 0.138 in CodeBLEU.
• Score: 25.812942624520694
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have behaved well in function-level code translation without repository-level context. However, the performance of LLMs in repository-level context code translation remains suboptimal due to complex dependencies and context, hindering their adoption in industrial settings. In this work, we propose a novel LLM-based code translation technique K-Trans, which leverages triple knowledge augmentation to enhance LLM's translation quality under repository context in real-world software development. First, K-Trans constructs a translation knowledge base by extracting relevant information from target-language codebases, the repository being translated, and prior translation results. Second, for each function to be translated, K-Trans retrieves relevant triple knowledge, including target-language code samples, dependency usage examples, and successful translation function pairs, serving as references to enhance LLM for translation. Third, K-Trans constructs a knowledge-augmented translation prompt using the retrieved triple knowledge and employs LLMs to generate the translated code while preserving repository context. It further leverages LLMs for self-debugging, enhancing translation correctness. The experiments show that K-Trans substantially outperforms the baseline adapted from previous work by 19.4%/40.2% relative improvement in pass@1 and 0.138 in CodeBLEU. It is important to note that the results also demonstrate that each knowledge significantly contributes to K-Trans's effectiveness in handling repository-level context code translation, with dependency usage examples making the most notable contribution. Moreover, as the self-evolution process progresses, the knowledge base continuously enhances the LLM's performance across various aspects of the repository-level code translation.

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