A Metrics-Oriented Architectural Model to Characterize Complexity on Machine Learning-Enabled Systems (CAIN 2025 - Doctoral Symposium)

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CAIN 2025 Doctoral Symposium

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When

Sun 27 Apr 2025 16:35 - 16:40 at 208 - Doctoral Symposium Talks Chair(s): Jennifer Horkoff
Mon 28 Apr 2025 15:00 - 15:20 at 212 - Doctoral Symposium 2 (Detailed Presentation)

Abstract

How can the complexity of ML-enabled systems be managed effectively? The goal of this research is to investigate how complexity affects ML-Enabled Systems (MLES). To address this question, this research aims to introduce a metrics-based architectural model to characterize the complexity of MLES. The goal of the metrics is to support architectural decisions, providing a guideline for the inception and evolution of these systems.

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Session Program

Sun 27 Apr
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16:00 - 17:30	Doctoral Symposium TalksDoctoral Symposium / Research and Experience Papers at 208 Chair(s): Jennifer Horkoff Chalmers and the University of Gothenburg

16:00 5m Talk		Optimizing Data Analytics Workflows through User-driven Experimentation: Progress and Updates Doctoral Symposium Keerthiga Rajenthiram Vrije Universiteit Amsterdam
16:05 5m Talk		CoCo Challenges in ML Engineering Teams: How to Collaboratively Build ML-Enabled Systems Doctoral Symposium Aidin Azamnouri Technical University of Munich
16:10 5m Talk		Towards a Privacy-by-Design Framework for ML-Enabled Systems Doctoral Symposium Yorick Sens Ruhr University Bochum
16:15 5m Talk		Towards an Adoption Framework to Foster Trust in AI-Assisted Software Engineering Doctoral Symposium Marvin Muñoz Barón Technical University of Munich
16:20 5m Talk		A Holistic Framework for Evolving AI-based Systems Doctoral Symposium Merel Veracx Fontys University of Applied Sciences
16:25 5m Talk		Assessing and Enhancing the Robustness of LLM-based Multi-Agent Systems Through Chaos Engineering. Doctoral Symposium Joshua Segun Owotogbe JADS/Tilburg University
16:30 5m Talk		Designing ML-Enabled Software Systems with ML Model Composition: A Green AI Perspective Doctoral Symposium Rumbidzai Chitakunye Vrije Universiteit Amsterdam
16:35 5m Talk		A Metrics-Oriented Architectural Model to Characterize Complexity on Machine Learning-Enabled Systems Doctoral Symposium Renato Cordeiro Ferreira University of São Paulo
16:40 5m Talk		Identification and Optimization of Redundant Code Using Large Language Models Doctoral Symposium Shamse Tasnim Cynthia University of Saskatchewan
16:45 5m Talk		Systematic Testing of Security-Related Defects in LLM-Based Applications Doctoral Symposium Hasan Kaplan Jheronimus Academy of Data Science, Tilburg University
16:50 5m Talk		Model-Based Verification for AI-Enabled Cyber-Physical Systems through Guided Falsification of Temporal Logic Properties Doctoral Symposium Hadiza Yusuf University of Michigan - Dearborn
16:55 35m Other		Open SC meeting Research and Experience Papers

Mon 28 Apr
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14:00 - 15:30	Doctoral Symposium 2 (Detailed Presentation)Doctoral Symposium at 212

14:00 20m Talk		A Holistic Framework for Evolving AI-based Systems Doctoral Symposium Merel Veracx Fontys University of Applied Sciences
14:20 20m Talk		Assessing and Enhancing the Robustness of LLM-based Multi-Agent Systems Through Chaos Engineering. Doctoral Symposium Joshua Segun Owotogbe JADS/Tilburg University
14:40 20m Talk		Designing ML-Enabled Software Systems with ML Model Composition: A Green AI Perspective Doctoral Symposium Rumbidzai Chitakunye Vrije Universiteit Amsterdam
15:00 20m Talk		A Metrics-Oriented Architectural Model to Characterize Complexity on Machine Learning-Enabled Systems Doctoral Symposium Renato Cordeiro Ferreira University of São Paulo

A Metrics-Oriented Architectural Model to Characterize Complexity on Machine Learning-Enabled Systems

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Sun 27 AprDisplayed time zone: Eastern Time (US & Canada) change

Mon 28 AprDisplayed time zone: Eastern Time (US & Canada) change

Renato Cordeiro Ferreira

University of São Paulo

Sun 27 Apr
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Mon 28 Apr
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