# Requirements Engineering for Artificial Intelligence Systems: A Systematic Mapping Study

arXiv: https://arxiv.org/abs/2212.10693 · HTML: https://mlsanity.com/paper/2212.10693/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2022.

## Built on by (most cited first, 14 of 14)

- [Model-driven Engineering for Machine Learning Components: A Systematic Literature Review](https://mlsanity.com/paper/2311.00284/) (2023)
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- [Dealing with Data for RE: Mitigating Challenges while using NLP and Generative AI](https://mlsanity.com/paper/2402.16977/) (2024)
- [Defining Quality Requirements for a Trustworthy AI Wildflower Monitoring Platform](https://mlsanity.com/paper/2303.13151/) (2023)
- [Enriching Moral Perspectives on AI: Concepts of Trust amongst Africans](https://mlsanity.com/paper/2508.14116/) (2025)
- [Requirements Engineering for Older Adult Digital Health Software: A Systematic Literature Review](https://mlsanity.com/paper/2411.03656/) (2024)
- [Bridging the AI Trustworthiness Gap between Functions and Norms](https://mlsanity.com/paper/2512.20671/) (2025)
- [AI Assurance in UK Defence: Challenges in Operationalising JSP 936](https://mlsanity.com/paper/2606.09414/) (2026)
- [Self-Explainability in Self-Adaptive and Self-Organising Systems: Status and Research Directions](https://mlsanity.com/paper/2606.09568/) (2026)
- [Specifying the Delegated-Autonomy Boundary: Requirements Engineering for Agentic AI](https://mlsanity.com/paper/2607.17225/) (2026)
- [Software Engineering for AI-driven Building Operation](https://mlsanity.com/paper/2608.16237/) (2026)
- [Regret Dominates Surprise: Design-Time Requirements Engineering for Agentic-AI Safety](https://mlsanity.com/paper/2609.05692/) (2026)

## Abstract (arXiv)

[Context] In traditional software systems, Requirements Engineering (RE) activities are well-established and researched. However, building Artificial Intelligence (AI) based software with limited or no insight into the system's inner workings poses significant new challenges to RE. Existing literature has focused on using AI to manage RE activities, with limited research on RE for AI (RE4AI). [Objective] This paper investigates current approaches for specifying requirements for AI systems, identifies available frameworks, methodologies, tools, and techniques used to model requirements, and finds existing challenges and limitations. [Method] We performed a systematic mapping study to find papers on current RE4AI approaches. We identified 43 primary studies and analysed the existing methodologies, models, tools, and techniques used to specify and model requirements in real-world scenarios. [Results] We found several challenges and limitations of existing RE4AI practices. The findings highlighted that current RE applications were not adequately adaptable for building AI systems and emphasised the need to provide new techniques and tools to support RE4AI. [Conclusion] Our results showed that most of the empirical studies on RE4AI focused on autonomous, self-driving vehicles and managing data requirements, and areas such as ethics, trust, and explainability need further research.