# AI for Requirements Engineering: Industry adoption and Practitioner perspectives

arXiv: https://arxiv.org/abs/2511.01324 · HTML: https://mlsanity.com/paper/2511.01324/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 25 references that are in the map.
- First posted on arXiv 3 Nov 2025.

## Builds on (most cited first, 12 of 12)

- [Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey](https://mlsanity.com/paper/2305.18703/) (2023)
- [Advancing Requirements Engineering through Generative AI: Assessing the Role of LLMs](https://mlsanity.com/paper/2310.13976/) (2023)
- [Investigating ChatGPT's Potential to Assist in Requirements Elicitation Processes](https://mlsanity.com/paper/2307.07381/) (2023)
- [Human-AI collaboration is not very collaborative yet: A taxonomy of interaction patterns in AI-assisted decision making from a systematic review](https://mlsanity.com/paper/2310.19778/) (2023)
- [Model Generation with LLMs: From Requirements to UML Sequence Diagrams](https://mlsanity.com/paper/2404.06371/) (2024)
- [Generative AI for Requirements Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2409.06741/) (2024)
- [Naming the Pain in Requirements Engineering: Contemporary Problems, Causes, and Effects in Practice](https://mlsanity.com/paper/1611.10288/) (2016)
- [Human AI Collaboration in Software Engineering: Lessons Learned from a Hands On Workshop](https://mlsanity.com/paper/2312.10620/) (2023)
- [Exploring LLMs for Verifying Technical System Specifications Against Requirements](https://mlsanity.com/paper/2411.11582/) (2024)
- [Using Large Language Models for Natural Language Processing Tasks in Requirements Engineering: A Systematic Guideline](https://mlsanity.com/paper/2402.13823/) (2024)
- [Normative Requirements Operationalization with Large Language Models](https://mlsanity.com/paper/2404.12335/) (2024)
- [The Influence of Human Aspects on Requirements Engineering-related Activities: Software Practitioners Perspective](https://mlsanity.com/paper/2109.07868/) (2021)

## Abstract (arXiv)

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.