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

arXiv: https://arxiv.org/abs/2510.04380 · HTML: https://mlsanity.com/paper/2510.04380/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 69 references that are in the map.
- First posted on arXiv 5 Oct 2025.

## Builds on (most cited first, 23 of 23)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Semantics derived automatically from language corpora contain human-like biases](https://mlsanity.com/paper/1608.07187/) (2016)
- [Fairness And Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, And Mitigation Strategies](https://mlsanity.com/paper/2304.07683/) (2023)
- [A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions](https://mlsanity.com/paper/2410.12837/) (2024)
- [AutoFreeze: Automatically Freezing Model Blocks to Accelerate Fine-tuning](https://mlsanity.com/paper/2102.01386/) (2021)
- [Advancing Requirements Engineering through Generative AI: Assessing the Role of LLMs](https://mlsanity.com/paper/2310.13976/) (2023)
- [Engineering AI Systems: A Research Agenda](https://mlsanity.com/paper/2001.07522/) (2020)
- [A Systematic Literature Review of User Trust in AI-Enabled Systems: An HCI Perspective](https://mlsanity.com/paper/2304.08795/) (2023)
- [Semantically Enhanced Software Traceability Using Deep Learning Techniques](https://mlsanity.com/paper/1804.02438/) (2018)
- [Requirements Engineering Challenges in Building AI-Based Complex Systems](https://mlsanity.com/paper/1908.11791/) (2019)
- [Quality Assurance for AI-based Systems: Overview and Challenges](https://mlsanity.com/paper/2102.05351/) (2021)
- [Reinforcement Learning for Test Case Prioritization](https://mlsanity.com/paper/2011.01834/) (2020)
- [Tailoring Requirements Engineering for Responsible AI](https://mlsanity.com/paper/2302.10816/) (2023)
- [Quality Management of Machine Learning Systems](https://mlsanity.com/paper/2006.09529/) (2020)
- [Natural Language Processing for Requirements Traceability](https://mlsanity.com/paper/2405.10845/) (2024)
- [Bridging the Research-Practice Gap in Requirements Engineering through Effective Teaching and Peer Learning](https://mlsanity.com/paper/1407.4186/) (2014)
- [AI Techniques for Software Requirements Prioritization](https://mlsanity.com/paper/2108.00832/) (2021)
- [Distributed Ledger for Provenance Tracking of Artificial Intelligence Assets](https://mlsanity.com/paper/2002.11000/) (2020)
- [Dealing with Data for RE: Mitigating Challenges while using NLP and Generative AI](https://mlsanity.com/paper/2402.16977/) (2024)
- [Grand Challenges of Traceability: The Next Ten Years](https://mlsanity.com/paper/1710.03129/) (2017)
- [Impact of requirements volatility on software architecture: How do software teams keep up with ever-changing requirements?](https://mlsanity.com/paper/1904.08164/) (2019)

## 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.