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Requirements Engineering for Machine Learning: Perspectives from Data Scientists
Technische Universit├Ąt Berlin, Germany.
RISE - Research Institutes of Sweden, ICT, SICS.ORCID iD: 0000-0001-7879-4371
2019 (English)In: Article in journal (Refereed) In press
Abstract [en]

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.

Place, publisher, year, edition, pages
2019.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:ri:diva-40582OAI: oai:DiVA.org:ri-40582DiVA, id: diva2:1363642
Available from: 2019-10-22 Created: 2019-10-22 Last updated: 2019-10-22

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