Knowledge-Guided Multi-Agent Framework for Automated Requirements Development: A Vision
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866913916844507136 |
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| author | Huang, Jiangping Jin, Dongming Sun, Weisong Liu, Yang Jin, Zhi |
| author_facet | Huang, Jiangping Jin, Dongming Sun, Weisong Liu, Yang Jin, Zhi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_22656 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Knowledge-Guided Multi-Agent Framework for Automated Requirements Development: A Vision Huang, Jiangping Jin, Dongming Sun, Weisong Liu, Yang Jin, Zhi Software Engineering Artificial Intelligence 68-04 D.2.3; I.2.7 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. |
| title | Knowledge-Guided Multi-Agent Framework for Automated Requirements Development: A Vision |
| topic | Software Engineering Artificial Intelligence 68-04 D.2.3; I.2.7 |
| url | https://arxiv.org/abs/2506.22656 |