Requirements Engineering for Machine Learning: Perspectives from Data Scientists
This addresses the challenge of developing effective requirements engineering methodologies for ML-based systems, which is an incremental step towards improving software development practices in this domain.
The paper tackles the problem of adapting requirements engineering for machine learning systems by interviewing data scientists, finding that the shift from coding to training necessitates changes in RE processes, including understanding ML performance measures and new quality requirements like explainability.
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.