UMLCoder: A Novel Multi-Agent Framework for Generating Code with the Help of uml Diagrams

Abstract

While Transformer-based Large Language Models (LLMs) have advanced NLP, achieving an efficient automated development workflow in multi-agent systems remains challenging due to issues in code accuracy, testing effectiveness, and agent collaboration. To address these limitations, we propose UMLCoder, a novel multi-agent collaborative code generation framework designed to enhance software reliability and maintainability. UMLCoder comprises four specialized agents: a UML Expert Agent for generating precise structural diagrams to guide programming, a Code Generation Agent, a Test Case Generation Agent, and a Test Execution Agent for robustness verification. Benchmark evaluations demonstrate that UMLCoder achieves a pass@1 score of 71.9% on HumanEval and 70.3% on MBPP. Compared to baselines like GPT-3.5-turbo and LLaMA3, the proposed framework significantly improves code quality and computational efficiency, reducing the time complexity from $O\left(n^{2}\right)$ to $O(n)$ in specific scenarios.