Supporting Stakeholder Requirements Expression with LLM Revisions: An Empirical Evaluation

TL;DR

This study evaluates how Large Language Models can assist stakeholders in articulating requirements more clearly and accurately, especially for those with limited domain knowledge, through an empirical user study.

Contribution

It introduces and empirically tests a stakeholder-centered approach using LLMs to improve requirement expression and validation, demonstrating enhanced clarity and alignment.

Findings

LLM revisions rated higher than original statements in alignment and clarity

Participants found LLM revisions helped surface tacit details and improve understanding

The approach promotes responsible AI use in requirements engineering

Abstract

Stakeholders often struggle to accurately express their requirements due to articulation barriers arising from limited domain knowledge or from cognitive constraints. This can cause misalignment between expressed and intended requirements, complicating elicitation and validation. Traditional elicitation techniques, such as interviews and follow-up sessions, are time-consuming and risk distorting stakeholders' original intent across iterations. Large Language Models (LLMs) can infer user intentions from context, suggesting potential for assisting stakeholders in expressing their needs. This raises the questions of (i) how effectively LLMs can support requirement expression and (ii) whether such support benefits stakeholders with limited domain expertise. We conducted a study with 26 participants who produced 130 requirement statements. Each participant first expressed requirements unaided, then evaluated LLM-generated revisions tailored to their context. Participants rated LLM revisions significantly higher than their original statements across all dimensions-alignment with intent, readability, reasoning, and unambiguity. Qualitative feedback further showed that LLM revisions often surfaced tacit details stakeholders considered important and helped them better understand their own requirements. We present and evaluate a stakeholder-centered approach that leverages LLMs as articulation aids in requirements elicitation and validation. Our results show that LLM-assisted reformulation improves perceived completeness, clarity, and alignment of requirements. By keeping stakeholders in the validation loop, this approach promotes responsible and trustworthy use of AI in Requirements Engineering.