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Non-functional requirements for machine learning: understanding current use and challenges among practitioners
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- Habibullah, Khan Mohammad (author)
- Gothenburg University,Göteborgs universitet,Institutionen för data- och informationsteknik, datavetenskap (GU),Department of Computer Science and Engineering, Computing Science (GU),University of Gothenburg
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- Gay, Gregory, 1987 (author)
- Gothenburg University,Göteborgs universitet,Institutionen för data- och informationsteknik (GU),Department of Computer Science and Engineering (GU),University of Gothenburg
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- Horkoff, Jennifer, 1980 (author)
- Gothenburg University,Göteborgs universitet,Institutionen för data- och informationsteknik (GU),Department of Computer Science and Engineering (GU),University of Gothenburg
- (creator_code:org_t)
- 2023-01-07
- 2023
- English.
- In: Requirements Engineering. - : Springer Science and Business Media LLC. - 0947-3602 .- 1432-010X. ; 28, s. 283-316
- Journal article (peer-reviewed)
Abstract
Subject headings
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- Systems that rely on Machine Learning (ML systems) have differing demands on quality—known as non-functional requirements (NFRs)—from traditional systems. NFRs for ML systems may differ in their definition, measurement, scope, and comparative importance. Despite the importance of NFRs in ensuring the quality ML systems, our understanding of all of these aspects is lacking compared to our understanding of NFRs in traditional domains. We have conducted interviews and a survey to understand how NFRs for ML systems are perceived among practitioners from both industry and academia. We have identified the degree of importance that practitioners place on different NFRs, including cases where practitioners are in agreement or have differences of opinion. We explore how NFRs are defined and measured over different aspects of a ML system (i.e., model, data, or whole system). We also identify challenges associated with NFR definition and measurement. Finally, we explore differences in perspective between practitioners in industry, academia, or a blended context. This knowledge illustrates how NFRs for ML systems are treated in current practice, and helps to guide future RE for ML efforts.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Programvaruteknik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Software Engineering (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences (hsv//eng)
Keyword
- Machine learning
- NFR Challenges
- NFRs
- Non-functional requirements
- Qualities
- Requirements engineering
- NFR Challenges
Publication and Content Type
- ref (subject category)
- art (subject category)
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