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Elicitron: An LLM Agent-Based Simulation Framework for Design Requirements Elicitation
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Requirements elicitation, a critical, yet time-consuming and challenging step in product development, often fails to capture the full spectrum of user needs. This may lead to products that fall short of expectations. This paper introduces a novel framework that leverages Large Language Models (LLMs) to automate and enhance the requirements elicitation process. LLMs are used to generate a vast array of simulated users (LLM agents), enabling the exploration of a much broader range of user needs and unforeseen use cases. These agents engage in product experience scenarios, through explaining their actions, observations, and challenges. Subsequent agent interviews and analysis uncover valuable user needs, including latent ones. We validate our framework with three experiments. First, we explore different methodologies for diverse agent generation, discussing their advantages and shortcomings. We measure the diversity of identified user needs and demonstrate that context-aware agent generation leads to greater diversity. Second, we show how our framework effectively mimics empathic lead user interviews, identifying a greater number of latent needs than conventional human interviews. Third, we showcase that LLMs can be used to analyze interviews, capture needs, and classify them as latent or not. Our work highlights the potential of using LLM agents to accelerate early-stage product development, reduce costs, and increase innovation.
Forward citations
Cited by 6 Pith papers
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iReDev: A Knowledge-Driven Multi-Agent Framework for Intelligent Requirements Development
A knowledge-driven, event-triggered multi-agent framework called iReDev generates software requirements artifacts that outperform zero-shot prompting, MetaGPT, and Elicitron on ten small projects.
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Requirements Elicitation Follow-Up Question Generation
GPT-4o-generated follow-up questions are rated equal to human questions when minimally guided, and better when guided by a 14-type interviewer mistake framework.
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Requirements Development and Formalization for Reliable Code Generation: A Multi-Agent Vision
The paper proposes ReDeFo, a multi-agent pipeline that uses formal specifications and verification to generate reliable code from natural language requirements.
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In a 2x2 experiment with 50 mock clients, conversational AI and choice-based responses improved response clarity but lowered perceived dependability of an elicitation tool.
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REConnect: Participatory RE for Social Sustainability
REConnect is a participatory requirements engineering framework, drawn from three community projects, that makes trust, co-design, and user empowerment the core of requirements work and assigns humans defined governan...
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Can LLMs Generate User Stories and Assess Their Quality?
LLMs generate user stories with human-like coverage and style but lower diversity, and they assess semantic quality well when given explicit codebook criteria, though human oversight is still needed.
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