大语言模型驱动的多智能体协同代码生成技术

doi:10.11896/jsjkx.241200033

Abstract

Abstract: In code generation tasks,pretrained large language models and agents have become key technologies for improving the quality and efficiency of code generation.However,when facing complex programming problems,intelligent agents based on large language models still struggle to provide effective solutions.This paper proposes a framework of multi-agent collaborative code generation to solve complex programming problems through the collaboration among agents,which includes four stages:problem analysis,task planning,code generation,and code debugging.The different base model strategies for agents based on open-source LLMs are proposed and the impact on system performance is tested.Additionally,an iterative programming paradigm incorporating reflection and debugging loops is introduced to optimize code generation based on feedback from each stage.Experimental results demonstrate that the multi-agent collaborative approach achieves significant performance improvements compared to traditional direct code generation methods across multiple datasets.Particularly,the hybrid model strategy achieves optimal perfor-mance on all tested datasets.Performance on test datasets is further improved with the adoption of reflection and debugging loops.

Key words: Multi-agent system, Large language model, Natural language processing, Code generation, Chain of thought

CLC Number:

TP391

XIA Peng, ZHANG Yijun, QI Ji. Multi-agent Collaborative Code Generation Technology Driven by Large Language Models[J].Computer Science, 2025, 52(11A): 241200033-9.

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Multi-agent Collaborative Code Generation Technology Driven by Large Language Models

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