mlsanityData to 14 Oct 2026

Reconsidering Requirements Engineering: Human-AI Collaboration in AI-Native Software Development

arXiv 2510.04380 ↗First posted 5 Oct 2025cs.SE cs.AI cs.HCcited by 0

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Abstract (arXiv)

Requirement Engineering (RE) is the foundation of successful software development. In RE, the goal is to ensure that implemented systems satisfy stakeholder needs through rigorous requirements elicitation, validation, and evaluation processes. Despite its critical role, RE continues to face persistent challenges, such as ambiguity, conflicting stakeholder needs, and the complexity of managing evolving requirements. A common view is that Artificial Intelligence (AI) has the potential to streamline the RE process, resulting in improved efficiency, accuracy, and management actions. However, using AI also introduces new concerns, such as ethical issues, biases, and lack of transparency. This paper explores how AI can enhance traditional RE practices by automating labor-intensive tasks, supporting requirement prioritization, and facilitating collaboration between stakeholders and AI systems. The paper also describes the opportunities and challenges that AI brings to RE. In particular, the vision calls for ethical practices in AI, along with a much-enhanced collaboration between academia and industry professionals. The focus should be on creating not only powerful but also trustworthy and practical AI solutions ready to adapt to the fast-paced world of software development.

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References · 23

  1. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  2. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,917
  3. 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,416
  4. 2016Semantics derived automatically from language corpora contain human-like biasescited by 901
  5. 2023Fairness And Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, And Mitigation Strategiescited by 85
  6. 2024A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directionscited by 70
  7. 2021AutoFreeze: Automatically Freezing Model Blocks to Accelerate Fine-tuningcited by 48
  8. 2023Advancing Requirements Engineering through Generative AI: Assessing the Role of LLMscited by 42
  9. 2020Engineering AI Systems: A Research Agendacited by 37
  10. 2023A Systematic Literature Review of User Trust in AI-Enabled Systems: An HCI Perspectivecited by 33
  11. 2018Semantically Enhanced Software Traceability Using Deep Learning Techniquescited by 22
  12. 2019Requirements Engineering Challenges in Building AI-Based Complex Systemscited by 21
11 more
  1. 2021Quality Assurance for AI-based Systems: Overview and Challengescited by 19
  2. 2020Reinforcement Learning for Test Case Prioritizationcited by 13
  3. 2023Tailoring Requirements Engineering for Responsible AIcited by 7
  4. 2020Quality Management of Machine Learning Systemscited by 6
  5. 2024Natural Language Processing for Requirements Traceabilitycited by 5
  6. 2014Bridging the Research-Practice Gap in Requirements Engineering through Effective Teaching and Peer Learningcited by 3
  7. 2021AI Techniques for Software Requirements Prioritizationcited by 3
  8. 2020Distributed Ledger for Provenance Tracking of Artificial Intelligence Assetscited by 2
  9. 2024Dealing with Data for RE: Mitigating Challenges while using NLP and Generative AIcited by 2
  10. 2017Grand Challenges of Traceability: The Next Ten Yearscited by 1
  11. 2019Impact of requirements volatility on software architecture: How do software teams keep up with ever-changing requirements?cited by 1

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