Enabling Machine Learning in software architecture frameworksMoin, 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 Full text not archived in this repository. It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.1109/cain58948.2023.00021 Abstract/SummarySeveral 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.
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