CodeChemist: Functional Knowledge Transfer for Low-Resource Code Generation via Test-Time Scaling
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908571654946816 |
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| author | Wang, Kaixin Li, Tianlin Zhang, Xiaoyu Liu, Aishan Liu, Xianglong Liu, Ziqi Zhang, Zhiqiang Zhou, Jun Shi, and Bin |
| author_facet | Wang, Kaixin Li, Tianlin Zhang, Xiaoyu Liu, Aishan Liu, Xianglong Liu, Ziqi Zhang, Zhiqiang Zhou, Jun Shi, and Bin |
| contents | Code Large Language Models (CodeLLMs) are increasingly used in code generation tasks across a wide range of applications. However, their performance is often inconsistent across different programming languages (PLs), with low-resource PLs suffering the most due to limited training data. In this paper, we present CodeChemist, a novel and efficient framework for test-time scaling that enables functional knowledge transfer from high-resource to low-resource PLs using generated test cases. CodeChemist first generates and executes code in high-resource PLs to create test cases that encapsulate functional knowledge. It then uses multi-temperature hedged sampling to generate code snippets in the low-resource PL and selects the best one based on the pass rate of the test cases. Our extensive experiments show that CodeChemist outperforms existing test-time scaling approaches, boosting the performance of code generation for low-resource PLs without requiring any model retraining. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_00501 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CodeChemist: Functional Knowledge Transfer for Low-Resource Code Generation via Test-Time Scaling Wang, Kaixin Li, Tianlin Zhang, Xiaoyu Liu, Aishan Liu, Xianglong Liu, Ziqi Zhang, Zhiqiang Zhou, Jun Shi, and Bin Software Engineering Code Large Language Models (CodeLLMs) are increasingly used in code generation tasks across a wide range of applications. However, their performance is often inconsistent across different programming languages (PLs), with low-resource PLs suffering the most due to limited training data. In this paper, we present CodeChemist, a novel and efficient framework for test-time scaling that enables functional knowledge transfer from high-resource to low-resource PLs using generated test cases. CodeChemist first generates and executes code in high-resource PLs to create test cases that encapsulate functional knowledge. It then uses multi-temperature hedged sampling to generate code snippets in the low-resource PL and selects the best one based on the pass rate of the test cases. Our extensive experiments show that CodeChemist outperforms existing test-time scaling approaches, boosting the performance of code generation for low-resource PLs without requiring any model retraining. |
| title | CodeChemist: Functional Knowledge Transfer for Low-Resource Code Generation via Test-Time Scaling |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2510.00501 |