Enabling Machine Learning in Software Architecture Frameworks

Armin Moin, Atta Badii, Stephan Gunnemann, Moharram Challenger

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE/ACM 2nd International Conference on AI Engineering - Software Engineering for AI, CAIN 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages92-93
Number of pages2
ISBN (Electronic)9798350301137
DOIs
StatePublished - 2023
Event2nd IEEE/ACM International Conference on AI Engineering - Software Engineering for AI, CAIN 2023 - Melbourne, Australia
Duration: 15 May 202316 May 2023

Publication series

NameProceedings - 2023 IEEE/ACM 2nd International Conference on AI Engineering - Software Engineering for AI, CAIN 2023

Conference

Conference2nd IEEE/ACM International Conference on AI Engineering - Software Engineering for AI, CAIN 2023
Country/TerritoryAustralia
CityMelbourne
Period15/05/2316/05/23

Keywords

  • architecture frameworks
  • machine learning
  • qualitative research
  • viewpoints
  • views

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