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Status Quo and Problems of Requirements Engineering for Machine Learning: Results from an International Survey
Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Brazil.
Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Brazil.
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.ORCID iD: 0000-0003-0619-6027
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2024 (English)In: Product-Focused Software Process Improvement: Proceedings, Part I / [ed] Regine Kadgien, Andreas Jedlitschka, Andrea Janes, Valentina Lenarduzzi, Xiaozhou Li, Springer Science+Business Media B.V., 2024, p. 159-174Conference paper, Published paper (Refereed)
Abstract [en]

Systems that use Machine Learning (ML) have become commonplace for companies that want to improve their products and processes. Literature suggests that Requirements Engineering (RE) can help address many problems when engineering ML-enabled systems. However, the state of empirical evidence on how RE is applied in practice in the context of ML-enabled systems is mainly dominated by isolated case studies with limited generalizability. We conducted an international survey to gather practitioner insights into the status quo and problems of RE in ML-enabled systems. We gathered 188 complete responses from 25 countries. We conducted quantitative statistical analyses on contemporary practices using bootstrapping with confidence intervals and qualitative analyses on the reported problems involving open and axial coding procedures. We found significant differences in RE practices within ML projects. For instance, (i) RE-related activities are mostly conducted by project leaders and data scientists, (ii) the prevalent requirements documentation format concerns interactive Notebooks, (iii) the main focus of non-functional requirements includes data quality, model reliability, and model explainability, and (iv) main challenges include managing customer expectations and aligning requirements with data. The qualitative analyses revealed that practitioners face problems related to lack of business domain understanding, unclear goals and requirements, low customer engagement, and communication issues. These results help to provide a better understanding of the adopted practices and of which problems exist in practical environments. We put forward the need to adapt further and disseminate RE-related practices for engineering ML-enabled systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Place, publisher, year, edition, pages
Springer Science+Business Media B.V., 2024. p. 159-174
Series
Lecture Notes in Computer Science (LNCS), ISSN 0302-9743, E-ISSN 1611-3349 ; 14483
Keywords [en]
Machine Learning, Requirements Engineering, Survey, Computer software selection and evaluation, Case-studies, Complete response, Confidence interval analysis, Contemporary practices, Engineering machines, International survey, Machine-learning, Qualitative analysis, Requirement engineering, Status quo
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:bth-27230DOI: 10.1007/978-3-031-49266-2_11Scopus ID: 2-s2.0-85190065443ISBN: 9783031492655 (print)OAI: oai:DiVA.org:bth-27230DiVA, id: diva2:1920262
Conference
24th International Conference on Product-Focused Software Process Improvement, PROFES 2023, Dornbirn, Dec 11-13, 2023
Available from: 2024-12-11 Created: 2024-12-11 Last updated: 2024-12-11Bibliographically approved

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Mendez, DanielLavesson, NiklasGorschek, Tony

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