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RKE-Coder: A LLMs-Based Code Generation Framework with Algorithmic and Code Knowledge Integration
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AI
DOI：10.1007/978-981-95-3343-5_41.png)
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Abstract
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Code generation tools are crucial for improving the efficiency and quality of computer programming. However, existing tools lack a deep understanding of algorithmic knowledge, leading to low-quality code logic in code generation problems. In this paper, we propose a novel framework for code generation, named RKE-Coder, designed to leverage domain knowledge to generate high-quality code embedded with algorithmic insights. The framework aims to enhance the quality and accuracy of code generation for computer programming problems with two types of representative knowledge: algorithmic knowledge and code knowledge. Experimental results show that the RKE-Coder successfully incorporates algorithmic knowledge into the generated code, improving the accuracy and quality of code produced by the LLMs for computer programming tasks. This research makes a significant contribution to competitive programming code generation. It provides a more powerful tool for programmers to address the ever-evolving challenges in computer programming problems.
Keywords:
Code Generation
Algorithmic and Code Knowledge
LLMs
Journal
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