A design science research approach to Large Language Model-Based Agents for Requirements Specification (LLMBA4RS) in low-code applications

A design science research approach to Large Language Model-Based Agents for Requirements Specification (LLMBA4RS) in low-code applications

연구 분야: Software Development

학회: Requirements Engineering

In today’s rapidly evolving markets, the pressure to accelerate software delivery while ensuring alignment with stakeholder needs is paramount. While Agile methodologies and Low-Code platforms have expedited the delivery process, the quality of requirements specification remains a critical component of project success. Poorly defined requirements necessitate repeated clarification cycles for developers, leading to sprint delays and increased project costs. To address this challenge, we propose the Large Language Model-Based Agents for Requirements Specification (LLMBA4RS) method, developed following a design science research approach. This method leverages Retrieval Augmented Generation (RAG) and the CrewAI framework to assist requirements engineers in crafting user stories based on minimal functional requirements. Through the demonstration and evaluation of three applications built on Low-Code platforms, practitioners assessed the method’s effectiveness in generating consistent requirements and suggesting related user stories.