DagRep.13.7.166.pdf
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Software Architecture and Machine Learning (Dagstuhl Seminar 23302)
Authors and all authors of the abstracts in this report
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Part of:
Issue:
Dagstuhl Reports, Volume 13, Issue 7
Volume: Dagstuhl Reports, Volume 13
Journal: Dagstuhl Reports (DagRep) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2024-03-08
Cite AsGet BibTex
Grace A. Lewis, Henry Muccini, Ipek Ozkaya, Karthik Vaidhyanathan, Roland Weiss, and Liming Zhu. Software Architecture and Machine Learning (Dagstuhl Seminar 23302). In Dagstuhl Reports, Volume 13, Issue 7, pp. 166-188, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/DagRep.13.7.166
https://doi.org/10.4230/DagRep.13.7.166
Abstract
This report documents the program and outcomes of Dagstuhl Seminar 23302, "Software Architecture and Machine Learning". We summarize the goals and format of the seminar, results from the breakout groups, key definitions relevant to machine learning-enabled systems that were discussed, and the research roadmap that emerged from the discussions during the seminar. The report also includes the abstracts of the talks presented at the seminar and summaries of open discussions.
Subject Classification
ACM Subject Classification
- Software and its engineering → Software architectures
- Computing methodologies → Machine learning
- Software and its engineering → Extra-functional properties
- Computing methodologies → Artificial intelligence
- Software and its engineering
Keywords
- Architecting ML-enabled Systems
- ML for Software Architecture
- Software Architecture for ML
- Machine Learning
- Software Architecture
- Software Engineering
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