AI在需求工程中以人机协作为主，助力软件开发更高效。
AI for Requirements Engineering: Industry adoption and Practitioner perspectives
- 采用问卷调研55名从业者，分析AI在需求四阶段的应用模式。
- 58.2%已用AI，人机协同占54.4%，全自动化仅5.4%。
- 强调需构建专用人机协作框架与负责任的AI治理机制。
人工智能在需求工程（RE）中的应用虽具显著优势，但也面临实际挑战。尽管需求工程是软件工程的基础环节，但针对其在AI应用方面的研究仍有限。本研究对55名软件从业者进行调查，梳理了AI在需求获取、分析、规约和验证四个阶段的应用情况，以及四种决策方式：纯人工、AI验证、人机协作（HAIC）和全自动化AI。受访者还分享了对AI应用的感知、挑战与机遇。数据显示，58.2%的受访者已在需求工程中使用AI，69.1%认为其影响为积极或非常积极。人机协作占据主导地位，占比达54.4%，而全自动化仅占5.4%；被动式AI验证（4.4%至6.2%）更低，表明从业者更重视AI的主动支持而非被动监督。结果表明，当AI作为协作伙伴而非替代者时，效果最佳。同时，随着应用深化，亟需构建面向需求工程的专用人机协作框架及稳健、负责任的AI治理机制。
原文摘要 · Abstract (English)
The integration of AI for Requirements Engineering (RE) presents significant benefits but also poses real challenges. Although RE is fundamental to software engineering, limited research has examined AI adoption in RE. We surveyed 55 software practitioners to map AI usage across four RE phases: Elicitation, Analysis, Specification, and Validation, and four approaches for decision making: human-only decisions, AI validation, Human AI Collaboration (HAIC), and full AI automation. Participants also shared their perceptions, challenges, and opportunities when applying AI for RE tasks. Our data show that 58.2% of respondents already use AI in RE, and 69.1% view its impact as positive or very positive. HAIC dominates practice, accounting for 54.4% of all RE techniques, while full AI automation remains minimal at 5.4%. Passive AI validation (4.4 to 6.2%) lags even further behind, indicating that practitioners value AI's active support over passive oversight. These findings suggest that AI is most effective when positioned as a collaborative partner rather than a replacement for human expertise. It also highlights the need for RE-specific HAIC frameworks along with robust and responsible AI governance as AI adoption in RE grows.
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