mlsanityData to 7 Oct 2026

Requirements Engineering for Machine Learning: A Review and Reflection

arXiv 2210.00859 ↗First posted 3 Oct 2022cs.SE cs.LGcited by 7

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Abstract (arXiv)

Today, many industrial processes are undergoing digital transformation, which often requires the integration of well-understood domain models and state-of-the-art machine learning technology in business processes. However, requirements elicitation and design decision making about when, where and how to embed various domain models and end-to-end machine learning techniques properly into a given business workflow requires further exploration. This paper aims to provide an overview of the requirements engineering process for machine learning applications in terms of cross domain collaborations. We first review the literature on requirements engineering for machine learning, and then go through the collaborative requirements analysis process step-by-step. An example case of industrial data-driven intelligence applications is also discussed in relation to the aforementioned steps.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 55 of its 193 references that are in the map. First posted on arXiv 3 Oct 2022.

Builds on

55 of 193 references.

References · 55

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38 more
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50 most cited of 55 All 55 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 2; 2026: 3.

7 papers.

  1. 2023Challenges and Opportunities of Using Transformer-Based Multi-Task Learning in NLP Through ML Lifecycle: A Surveycited by 1
  2. 2024A Mathematical Model of the Hidden Feedback Loop Effect in Machine Learning Systemscited by 1
  3. 2026Verifying Machine Learning Interpretability Requirements through Provenancecited by 1
  4. 2025A Guide to Failure in Machine Learning: Reliability and Robustness from Foundations to Practicecited by 0
  5. 2025Is PMBOK Guide the Right Fit for AI? Re-evaluating Project Management in the Face of Artificial Intelligence Projectscited by 0
  6. 2026Boosting AI Reliability with an FSM-Driven Streaming Inference Pipeline: An Industrial Casecited by 0
  7. 2026From Failure to Alignment: A Requirements Engineering Framework for Machine Learning Systemscited by 0