Towards Multi-Agent Systems in Requirements Engineering: A Proof-of-Concept Study Using Large Language Models

Requirements Engineering (RE) is a critical yet time-consuming phase in software development, often hindered by inconsistencies and inaccuracies. We propose a novel framework that combines Foundation Models (FMs) and Multi-Agent Systems (MAS) to enhance RE efficiency, accuracy, and quality. Our framework aims to automate routine tasks and provide intelligent assistance throughout all RE phases.
In a preliminary Proof-of-Concept (PoC) experiment, we explored the capabilities of Large Language Models (LLMs) in RE, evaluating their performance on various interlinked tasks across multiple phases. Our results showed that LLMs can achieve human-like accuracy in assessing requirements deliverables, highlighting the importance of suitable LLMs for each phase. We discuss the implications of our findings and outline a research agenda to address the limitations of FM-based MAS in RE, focusing on key objectives such as data availability, model calibration, and human-AI collaboration. Our goal is to create a more efficient, collaborative, and reliable RE process.