AI for Requirements Engineering: Industry adoption and Practitioner perspectives
Lekshmi Murali Rani, Richard Berntsson Svensson, Robert Feldt
TL;DR
The paper addresses the lack of empirical understanding of AI adoption in Requirements Engineering (RE) by surveying 55 practitioners to map AI usage across the four RE phases (elicitation, analysis, specification, validation) and four decision approaches (human-only, AI validation, HAIC, full automation). It finds that 58.2% of respondents use AI in RE and 69.1% view its impact positively, with HAIC dominating (≈50–60% of techniques) and full automation remaining rare (~5%). Attitudes toward AI are more positive among AI users, and practitioners favor collaborative AI over autonomous AI, urging domain-specific HAIC frameworks and responsible AI governance. The study highlights governance gaps and practical barriers, arguing that robust, domain-aware HAIC approaches are key to scalable, trustworthy AI adoption in RE. The results have practical implications for practitioners, researchers, and tool developers aiming to integrate AI into RE tasks responsibly and effectively.
Abstract
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.