用六个专家代理构建需求自动化框架，提升开发效率与准确性。
Knowledge-Guided Multi-Agent Framework for Automated Requirements Development: A Vision
- 六类专用智能体协同工作，各司其职完成需求任务。
- 通过概念化资源池设计，实现需求文档的高效生成与管理。
- 适合研究需求自动化与多智能体系统融合的学者与工程师。
本文提出一种名为KGMAF的知识引导多智能体框架，用于自动化需求开发。当前软件工程自动化系统多聚焦代码生成，忽视需求任务的复杂性。KGMAF由六个专业化智能体和一个资源池组成，旨在提升效率与准确性。论文详细说明了各智能体的功能、行为及所需知识，并给出了资源池的概念设计。案例研究表明，KGMAF在真实场景中具有应用潜力。最后，文章指出了基于多智能体系统实现与优化自动化需求开发的若干研究方向。我们认为，KGMAF将在大模型时代塑造自动化需求开发的未来。
原文摘要 · Abstract (English)
This paper envisions a knowledge-guided multi-agent framework named KGMAF for automated requirements development. KGMAF aims to address gaps in current automation systems for SE, which prioritize code development and overlook the complexities of requirements tasks. KGMAF is composed of six specialized agents and an artifact pool to improve efficiency and accuracy. Specifically, KGMAF outlines the functionality, actions, and knowledge of each agent and provides the conceptual design of the artifact pool. Our case study highlights the potential of KGMAF in real-world scenarios. Finally, we outline several research opportunities for implementing and enhancing automated requirements development using multi-agent systems. We believe that KGMAF will play a pivotal role in shaping the future of automated requirements development in the era of LLMs.
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