mlsanityData to 7 Oct 2026

Requirement Engineering Challenges for AI-intense Systems Development

arXiv 2103.10270 ↗First posted 18 Mar 2021cs.LG cs.AIcited by 6

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Abstract (arXiv)

Availability of powerful computation and communication technology as well as advances in artificial intelligence enable a new generation of complex, AI-intense systems and applications. Such systems and applications promise exciting improvements on a societal level, yet they also bring with them new challenges for their development. In this paper we argue that significant challenges relate to defining and ensuring behaviour and quality attributes of such systems and applications. We specifically derive four challenge areas from relevant use cases of complex, AI-intense systems and applications related to industry, transportation, and home automation: understanding, determining, and specifying (i) contextual definitions and requirements, (ii) data attributes and requirements, (iii) performance definition and monitoring, and (iv) the impact of human factors on system acceptance and success. Solving these challenges will imply process support that integrates new requirements engineering methods into development approaches for complex, AI-intense systems and applications. We present these challenges in detail and propose a research roadmap.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 12 of its 45 references that are in the map. First posted on arXiv 18 Mar 2021.

Builds on

12 of 45 references.

References · 12

  1. 2018Model Cards for Model Reportingcited by 1,068
  2. 2018Safely Entering the Deep: A Review of Verification and Validation for Machine Learning and a Challenge Elicitation in the Automotive Industrycited by 38
  3. 2020Engineering AI Systems: A Research Agendacited by 37
  4. 2020Safety Concerns and Mitigation Approaches Regarding the Use of Deep Learning in Safety-Critical Perception Taskscited by 31
  5. 2020Design Science Research Process: A Model for Producing and Presenting Information Systems Researchcited by 5
  6. 2019Boundary Objects and their Use in Agile Systems Engineeringcited by 3
  7. 2018T-Reqs: Tool Support for Managing Requirements in Large-Scale Agile System Developmentcited by 2
  8. 2019LEGaTO: Low-Energy, Secure, and Resilient Toolset for Heterogeneous Computingcited by 2
  9. 2019Agility is responsiveness to change: An essential definitioncited by 1
  10. 2020Why and How to Balance Alignment and Diversity of Requirements Engineering Practices in Automotivecited by 1
  11. 2020Charting Coordination Needs in Large-Scale Agile Organisationswith Boundary Objects and Methodological Islandscited by 1
  12. 2020Constructive Master's Thesis Work in Industry: Guidelines for Applying Design Science Researchcited by 1

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Cited by year

Citing papers per year: 2022: 3; 2023: 0; 2024: 2; 2025: 1.

6 papers.

  1. 2022Requirements Engineering for Machine Learning: A Review and Reflectioncited by 7
  2. 2024How Mature is Requirements Engineering for AI-based Systems? A Systematic Mapping Study on Practices, Challenges, and Future Research Directionscited by 5
  3. 2022A Compositional Approach to Creating Architecture Frameworks with an Application to Distributed AI Systemscited by 4
  4. 2022RESAM: Requirements Elicitation and Specification for Deep-Learning Anomaly Models with Applications to UAV Flight Controllerscited by 3
  5. 2024Requirements Engineering for Older Adult Digital Health Software: A Systematic Literature Reviewcited by 0
  6. 2025Towards User-Centred Design of AI-Assisted Decision-Making in Law Enforcementcited by 0