# Requirements Engineering for Machine Learning: Perspectives from Data Scientists

arXiv: https://arxiv.org/abs/1908.04674 · HTML: https://mlsanity.com/paper/1908.04674/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2019.

## Built on by (most cited first, 30 of 49)

- [From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future](https://mlsanity.com/paper/2408.02479/) (2024)
- [Software Engineering for AI-Based Systems: A Survey](https://mlsanity.com/paper/2105.01984/) (2021)
- [Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Process](https://mlsanity.com/paper/2110.10234/) (2021)
- [Evaluation Gaps in Machine Learning Practice](https://mlsanity.com/paper/2205.05256/) (2022)
- [A Meta-Summary of Challenges in Building Products with ML Components -- Collecting Experiences from 4758+ Practitioners](https://mlsanity.com/paper/2304.00078/) (2023)
- [Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering](https://mlsanity.com/paper/2209.04963/) (2022)
- [A Synthesis of Green Architectural Tactics for ML-Enabled Systems](https://mlsanity.com/paper/2312.09610/) (2023)
- [Ergo, SMIRK is Safe: A Safety Case for a Machine Learning Component in a Pedestrian Automatic Emergency Brake System](https://mlsanity.com/paper/2204.07874/) (2022)
- [Management of Machine Learning Lifecycle Artifacts: A Survey](https://mlsanity.com/paper/2210.11831/) (2022)
- [SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detection](https://mlsanity.com/paper/2305.00550/) (2023)
- [If a Human Can See It, So Should Your System: Reliability Requirements for Machine Vision Components](https://mlsanity.com/paper/2202.03930/) (2022)
- [Towards a Roadmap on Software Engineering for Responsible AI](https://mlsanity.com/paper/2203.08594/) (2022)
- [Beyond Testers' Biases: Guiding Model Testing with Knowledge Bases using LLMs](https://mlsanity.com/paper/2310.09668/) (2023)
- [Quality issues in Machine Learning Software Systems](https://mlsanity.com/paper/2208.08982/) (2022)
- [Requirements Engineering for Machine Learning: A Review and Reflection](https://mlsanity.com/paper/2210.00859/) (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)
- [Exploring the Assessment List for Trustworthy AI in the Context of Advanced Driver-Assistance Systems](https://mlsanity.com/paper/2103.09051/) (2021)
- [How Mature is Requirements Engineering for AI-based Systems? A Systematic Mapping Study on Practices, Challenges, and Future Research Directions](https://mlsanity.com/paper/2409.07192/) (2024)
- [Quality Assurance Challenges for Machine Learning Software Applications During Software Development Life Cycle Phases](https://mlsanity.com/paper/2105.01195/) (2021)
- [Agility in Software 2.0 -- Notebook Interfaces and MLOps with Buttresses and Rebars](https://mlsanity.com/paper/2111.14142/) (2021)
- [A Compositional Approach to Creating Architecture Frameworks with an Application to Distributed AI Systems](https://mlsanity.com/paper/2212.13570/) (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)
- [Automotive Perception Software Development: An Empirical Investigation into Data, Annotation, and Ecosystem Challenges](https://mlsanity.com/paper/2303.05947/) (2023)
- [Safety Case Templates for Autonomous Systems](https://mlsanity.com/paper/2102.02625/) (2021)
- [Test Automation with Grad-CAM Heatmaps -- A Future Pipe Segment in MLOps for Vision AI?](https://mlsanity.com/paper/2103.01837/) (2021)
- [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)
- [Setting AI in context: A case study on defining the context and operational design domain for automated driving](https://mlsanity.com/paper/2201.11451/) (2022)
- [Exploring ML testing in practice -- Lessons learned from an interactive rapid review with Axis Communications](https://mlsanity.com/paper/2203.16225/) (2022)

## Abstract (arXiv)

Machine learning (ML) is used increasingly in real-world applications. In this paper, we describe our ongoing endeavor to define characteristics and challenges unique to Requirements Engineering (RE) for ML-based systems. As a first step, we interviewed four data scientists to understand how ML experts approach elicitation, specification, and assurance of requirements and expectations. The results show that changes in the development paradigm, i.e., from coding to training, also demands changes in RE. We conclude that development of ML systems demands requirements engineers to: (1) understand ML performance measures to state good functional requirements, (2) be aware of new quality requirements such as explainability, freedom from discrimination, or specific legal requirements, and (3) integrate ML specifics in the RE process. Our study provides a first contribution towards an RE methodology for ML systems.