通过LLM修订支持利益相关者需求表达：一项实证评估
Supporting Stakeholder Requirements Expression with LLM Revisions: An Empirical Evaluation
AI总结：
本文通过实证研究展示LLM在辅助利益相关者表达需求方面的有效性，证明其能提升需求表达的完整性、清晰度和一致性。
摘要：
利益相关者由于领域知识有限或认知限制而难以准确表达需求，导致表达与意图不一致，增加了需求获取和验证的难度。传统的需求获取技术，如访谈和后续会议，耗时且可能在迭代过程中扭曲利益相关者的原始意图。大型语言模型（LLMs）可以从上下文中推断用户意图，这为帮助利益相关者表达需求提供了可能。本文探讨了（i）LLMs如何有效支持需求表达，以及（ii）这种支持是否对领域知识有限的利益相关者有益。我们与26名参与者进行了研究，他们产生了130条需求陈述。每位参与者首先无辅助地表达需求，然后评估针对其情境生成的LLM修订。参与者在所有维度上均对LLM修订的评分高于原始陈述，包括与意图的一致性、可读性、推理和无歧义性。定性反馈进一步显示，LLM修订经常揭示出利益相关者认为重要的隐性细节，并帮助他们更好地理解自己的需求。我们提出并评估了一种以利益相关者为中心的方法，利用LLM作为需求获取和验证中的表达辅助工具。我们的结果表明，LLM辅助的改写提高了对需求完整性的感知、清晰度和一致性。通过保持利益相关者在验证循环中，这种方法促进了在需求工程中负责任和可信地使用AI。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
