Enabling Machine Learning in Software Architecture Frameworks
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. Therefore, existing frameworks have failed to address the architecture viewpoints and views responsive to the concerns of the data science community. In this paper, we address this gap by establishing the architecture frameworks adapted to meet the requirements of modern applications and organizations where ML artifacts are both prevalent and crucial. We deploy empirical and qualitative research methods, such as a literature review and expert interviews, for example, through an online survey questionnaire. We interviewed 65 experts from around 25 organizations in over ten countries to devise and validate the proposed framework.
Mon 15 MayDisplayed time zone: Hobart change
20:45 - 22:15 | Poster - OnlinePosters / Papers at Virtual - Zoom for CAIN Chair(s): Mona Rahimi Northern Illinois University, Karthik Vaidhyanathan IIIT Hyderabad Click here to Join us over zoomClick Here to watch the session recording on YouTube | ||
20:45 6mPoster | AI Living Lab: Quality Assurance for AI-based Health systems Posters | ||
20:51 6mPoster | AI Planning Software Development Lifecycle Posters Ilche Georgievski University of Stuttgart, Germany File Attached | ||
20:57 6mPoster | Algorithm Debt: Challenges and Future Paths Posters Emmanuel Iko-Ojo Simon Australian National University, Melina Vidoni Australian National University, Fatemeh Hendijani Fard University of British Columbia | ||
21:03 6mPoster | Enabling Machine Learning in Software Architecture Frameworks Posters Armin Moin University of California, Santa Barbara, Atta Badii University of Reading, United Kingdom, Stephan G¨unnemann School of Computation, Information and Technology, Technical University of Munich, Munich, Germany, Moharram Challenger University of Antwerp DOI Pre-print | ||
21:09 6mPoster | Extensible Modeling Framework for Reliable Machine Learning System Analysis Posters Jati Hiliamsyah Husen Waseda University, Hironori Washizaki Waseda University, Hnin Thandar Tun Waseda University, Japan, Nobukazu Yoshioka Waseda University, Japan, Yoshiaki Fukazawa Waseda University, Hironori Takeuchi Musashi University, Hiroshi Tanaka Fujitsu Limited, Tokyo, Japan, Kazuki Munakata Fujitsu Limited, Tokyo, Japan | ||
21:15 6mPoster | How Federated Machine Learning Helps Increase the Mutual Benefit of Data-Sharing Ecosystems Posters Iva Krasteva Sofia University, GATE Institute, Boris Kraychev GATE Institute, Ensiye Kiyamousavi GATE Institute | ||
21:21 6mPoster | Maintaining and Monitoring AIOps Models Against Concept Drift Posters Lorena Poenaru-Olaru TU Delft, Luís Cruz Delft University of Technology, Jan S. Rellermeyer Leibniz University Hannover, Arie van Deursen Delft University of Technology | ||
21:27 6mPoster | Reproducibility Requires Consolidated Artifacts Posters Iordanis Fostiropoulos University of Southern California, USA, Bowman Brown University of Southern California, USA, Laurent Itti University of Southern California, USA | ||
21:33 6mPoster | Tenet: A Flexible Framework for Machine Learning-based Vulnerability Detection Posters Eduard Costel Pinconschi Instituto Superior Técnico, University of Lisboa & INESC-ID, Sofia Reis Instituto Superior Técnico, U. Lisboa & INESC-ID, Chi Zhang , Rui Abreu Faculty of Engineering, University of Porto, Hakan Erdogmus Carnegie Mellon University, Limin Jia Carnegie Mellon University | ||
21:39 6mPoster | Towards Understanding Machine Learning Testing in Practise Posters Arumoy Shome Delft University of Technology, Luís Cruz Delft University of Technology, Arie van Deursen Delft University of Technology Pre-print | ||
21:45 30mBreak | Break Out Session - Online Papers | ||