AI for Requirements Engineering: Industry adoption and Practitioner perspectives

2025-11-03

AI Summary

Despite growing AI adoption in Requirements Engineering (RE), there remains a lack of empirical understanding of how practitioners deploy AI across the four RE phases—elicitation, analysis, specification, and validation—and how they perceive its impact. Method: We conducted a global mixed-methods survey (N=XXX) to quantitatively assess the prevalence and effectiveness of four AI decision-making paradigms: fully manual, AI-assisted verification, human-AI collaborative (HAIC), and fully automated. Contribution/Results: 58.2% of respondents reported AI usage, with HAIC dominating (54.4%) and full automation remaining rare (5.4%); 69.1% perceived AI’s impact as positive. This study is the first to empirically identify HAIC as the prevailing paradigm in industrial RE practice. Building on this finding, we propose a RE-specific human-AI collaboration framework and principles for responsible AI governance, positioning AI as an “augmentative collaborative partner”—not a replacement—for requirements professionals. This reframing constitutes the paper’s core theoretical contribution and primary practical implication.

Technology Category

• Humans and AI: Human-AI Collaboration / Human-AI Teaming
• Knowledge Representation and Reasoning: Preferences
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Mapping AI adoption across requirements engineering phases
• Examining practitioner perspectives on human-AI collaboration
• Identifying challenges and opportunities in AI-driven requirements engineering

Innovation

• Human AI Collaboration dominates requirements engineering phases
• AI serves as collaborative partner not human replacement
• Requires specific HAIC frameworks with responsible governance