CodeChemist: Functional Knowledge Transfer for Low-Resource Code Generation via Test-Time Scaling

• URL: http://arxiv.org/abs/2510.00501v1
• Date: Wed, 01 Oct 2025 04:33:53 GMT
• Title: CodeChemist: Functional Knowledge Transfer for Low-Resource Code Generation via Test-Time Scaling
• Authors: Kaixin Wang, Tianlin Li, Xiaoyu Zhang, Aishan Liu, Xianglong Liu, Ziqi Liu, Zhiqiang Zhang, Jun Zhou, and Bin Shi,
• Abstract summary: We present CodeChemist, a framework for test-time scaling that enables functional knowledge transfer from high-resource to low-resource PLs.<n>Our experiments show that CodeChemist outperforms existing test-time scaling approaches.
• Score: 63.08126845138046
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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