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Enabling Machine Learning in software architecture frameworks

Moin, A., Badii, A., Günnemann, S. and Challenger, M. (2023) Enabling Machine Learning in software architecture frameworks. In: 2023 IEEE/ACM 2nd International Conference on AI Engineering – Software Engineering for AI (CAIN). IEEE. ISBN 9798350301137

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To link to this item DOI: 10.1109/cain58948.2023.00021


Several architecture frameworks for software, systems, and enterprises have been proposed in the literature. They have identified various stakeholders and defined architecture viewpoints and views to frame and address stakeholder concerns. However, the Machine Learning (ML) and data science-related concerns of data scientists and data engineers are yet to be included in existing architecture frameworks. We interviewed 65 experts from around 25 organizations in over ten countries to devise and validate the proposed framework that addresses the mentioned shortcoming.

Item Type:Book or Report Section
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:112595

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