Requirements Engineering for Machine Learning
A Study in Behavior-Driven Development
J.M. Rosenberg (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Cynthia Liem – Mentor (TU Delft - Multimedia Computing)
Antony Bartlett – Mentor (TU Delft - Multimedia Computing)
C.E. Brandt – Graduation committee member (TU Delft - Software Engineering)
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Abstract
Machine Learning (ML) systems are increasingly used in high-stakes, socially impactful domains, requiring attention to improve explainability and trust. However, current Requirements Engineering (RE) techniques often fail to address these human-centered qualities. This research investigates how Behavior-Driven Development (BDD) and Goal-Oriented Requirements Engineering (GORE) can improve the identification and visualization of requirements for explainability and trust. We conducted expert interviews and a survey to see how different stakeholders rate certain BDD scenarios and what they think of conceptual GORE models. Our results show that participants value concise, human-readable BDD scenarios and particularly like the GORE framework of i* to understand stakeholder relationships and system behaviors. The other GORE framework, GR4ML, was found to align more with business goals and addresses other stakeholder perspectives less. We conclude that BDD and GORE can improve explainability in ML system development. Future work should refine modeling tools to better integrate ethical and fairness considerations.