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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.09045 |
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| _version_ | 1866918180190945280 |
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| author | Chakraborty, Manojit Ghosh, Madhusudan Gupta, Rishabh |
| author_facet | Chakraborty, Manojit Ghosh, Madhusudan Gupta, Rishabh |
| contents | In the domain of software development, LLMs have been utilized to automate tasks such as code translation, where source code from one programming language is translated to another while preserving its functionality. However, LLMs often struggle with long source codes that don't fit into the context window, which produces inaccurate translations. To address this, we propose a novel zero-shot code translation method that incorporates identifier replacement. By substituting user-given long identifiers with generalized placeholders during translation, our method allows the LLM to focus on the logical structure of the code, by reducing token count and memory usage, which improves the efficiency and cost-effectiveness of long code translation. Our empirical results demonstrate that our approach preserves syntactical and hierarchical information and produces translation results with reduced tokens. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_09045 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LLM Based Long Code Translation using Identifier Replacement Chakraborty, Manojit Ghosh, Madhusudan Gupta, Rishabh Software Engineering Artificial Intelligence Information Retrieval Machine Learning In the domain of software development, LLMs have been utilized to automate tasks such as code translation, where source code from one programming language is translated to another while preserving its functionality. However, LLMs often struggle with long source codes that don't fit into the context window, which produces inaccurate translations. To address this, we propose a novel zero-shot code translation method that incorporates identifier replacement. By substituting user-given long identifiers with generalized placeholders during translation, our method allows the LLM to focus on the logical structure of the code, by reducing token count and memory usage, which improves the efficiency and cost-effectiveness of long code translation. Our empirical results demonstrate that our approach preserves syntactical and hierarchical information and produces translation results with reduced tokens. |
| title | LLM Based Long Code Translation using Identifier Replacement |
| topic | Software Engineering Artificial Intelligence Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2510.09045 |