# How Mature is Requirements Engineering for AI-based Systems? A Systematic Mapping Study on Practices, Challenges, and Future Research Directions arXiv: https://arxiv.org/abs/2409.07192 ยท HTML: https://mlsanity.com/paper/2409.07192/ ## Key facts - Cited by 5 papers in the map (data through 14 Oct 2026). - Builds on 33 of its 180 references that are in the map. - First posted on arXiv 11 Sep 2024. ## Builds on (most cited first, 30 of 33) - [Questioning the AI: Informing Design Practices for Explainable AI User Experiences](https://mlsanity.com/paper/2001.02478/) (2020) - [Software Engineering for AI-Based Systems: A Survey](https://mlsanity.com/paper/2105.01984/) (2021) - [Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needs](https://mlsanity.com/paper/2101.09824/) (2021) - ["Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction](https://mlsanity.com/paper/2210.03735/) (2022) - [Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Process](https://mlsanity.com/paper/2110.10234/) (2021) - [Requirements Engineering for Machine Learning: Perspectives from Data Scientists](https://mlsanity.com/paper/1908.04674/) (2019) - [Perfection Not Required? Human-AI Partnerships in Code Translation](https://mlsanity.com/paper/2104.03820/) (2021) - [Trust in AutoML: Exploring Information Needs for Establishing Trust in Automated Machine Learning Systems](https://mlsanity.com/paper/2001.06509/) (2020) - [Requirements Engineering Challenges in Building AI-Based Complex Systems](https://mlsanity.com/paper/1908.11791/) (2019) - [Towards Guidelines for Assessing Qualities of Machine Learning Systems](https://mlsanity.com/paper/2008.11007/) (2020) - [Software engineering for artificial intelligence and machine learning software: A systematic literature review](https://mlsanity.com/paper/2011.03751/) (2020) - [Explanation Ontology: A Model of Explanations for User-Centered AI](https://mlsanity.com/paper/2010.01479/) (2020) - [Towards a Framework to Manage Perceptual Uncertainty for Safe Automated Driving](https://mlsanity.com/paper/1903.03438/) (2019) - [If a Human Can See It, So Should Your System: Reliability Requirements for Machine Vision Components](https://mlsanity.com/paper/2202.03930/) (2022) - [Adapting SQuaRE for Quality Assessment of Artificial Intelligence Systems](https://mlsanity.com/paper/1908.02134/) (2019) - [Think About the Stakeholders First! Towards an Algorithmic Transparency Playbook for Regulatory Compliance](https://mlsanity.com/paper/2207.01482/) (2022) - [Tailoring Requirements Engineering for Responsible AI](https://mlsanity.com/paper/2302.10816/) (2023) - [Requirements Engineering Framework for Human-centered Artificial Intelligence Software Systems](https://mlsanity.com/paper/2303.02920/) (2023) - [Requirement Engineering Challenges for AI-intense Systems Development](https://mlsanity.com/paper/2103.10270/) (2021) - [Machine Learning Model Development from a Software Engineering Perspective: A Systematic Literature Review](https://mlsanity.com/paper/2102.07574/) (2021) - [Towards Artefact-based Requirements Engineering for Data-Centric Systems](https://mlsanity.com/paper/2103.05233/) (2021) - [Identifying Roles, Requirements and Responsibilities in Trustworthy AI Systems](https://mlsanity.com/paper/2106.08258/) (2021) - [Can Requirements Engineering Support Explainable Artificial Intelligence? Towards a User-Centric Approach for Explainability Requirements](https://mlsanity.com/paper/2206.01507/) (2022) - [An investigation of challenges encountered when specifying training data and runtime monitors for safety critical ML applications](https://mlsanity.com/paper/2301.13476/) (2023) - [Towards Risk Modeling for Collaborative AI](https://mlsanity.com/paper/2103.07460/) (2021) - [Pairing Conceptual Modeling with Machine Learning](https://mlsanity.com/paper/2106.14251/) (2021) - [Non-Functional Requirements for Machine Learning: An Exploration of System Scope and Interest](https://mlsanity.com/paper/2203.11063/) (2022) - [RESAM: Requirements Elicitation and Specification for Deep-Learning Anomaly Models with Applications to UAV Flight Controllers](https://mlsanity.com/paper/2207.08857/) (2022) - [Designer-User Communication for XAI: An epistemological approach to discuss XAI design](https://mlsanity.com/paper/2105.07804/) (2021) - [Towards Perspective-Based Specification of Machine Learning-Enabled Systems](https://mlsanity.com/paper/2206.09760/) (2022) ## Built on by (most cited first, 5 of 5) - [ADEMA: A Knowledge-State Orchestration Architecture for Long-Horizon Knowledge Synthesis with LLMAgents](https://mlsanity.com/paper/2604.25849/) (2026) - [Data Requirement Goal Modeling for Machine Learning Systems](https://mlsanity.com/paper/2504.07664/) (2025) - [Persona-Based Requirements Engineering for Explainable Multi-Agent Educational Systems: A Scenario Simulator for Clinical Reasoning Training](https://mlsanity.com/paper/2604.17186/) (2026) - [AI Assurance in UK Defence: Challenges in Operationalising JSP 936](https://mlsanity.com/paper/2606.09414/) (2026) - [Regret Dominates Surprise: Design-Time Requirements Engineering for Agentic-AI Safety](https://mlsanity.com/paper/2609.05692/) (2026) ## Abstract (arXiv) Artificial intelligence (AI) permeates all fields of life, which resulted in new challenges in requirements engineering for artificial intelligence (RE4AI), e.g., the difficulty in specifying and validating requirements for AI or considering new quality requirements due to emerging ethical implications. It is currently unclear if existing RE methods are sufficient or if new ones are needed to address these challenges. Therefore, our goal is to provide a comprehensive overview of RE4AI to researchers and practitioners. What has been achieved so far, i.e., what practices are available, and what research gaps and challenges still need to be addressed? To achieve this, we conducted a systematic mapping study combining query string search and extensive snowballing. The extracted data was aggregated, and results were synthesized using thematic analysis. Our selection process led to the inclusion of 126 primary studies. Existing RE4AI research focuses mainly on requirements analysis and elicitation, with most practices applied in these areas. Furthermore, we identified requirements specification, explainability, and the gap between machine learning engineers and end-users as the most prevalent challenges, along with a few others. Additionally, we proposed seven potential research directions to address these challenges. Practitioners can use our results to identify and select suitable RE methods for working on their AI-based systems, while researchers can build on the identified gaps and research directions to push the field forward.