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A Tale of Two Systems: Characterizing Architectural Complexity on Machine Learning-Enabled Systems
June 12, 2025 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Renato Cordeiro Ferreira
arXiv ID
2506.11295
Category
cs.SE: Software Engineering
Cross-listed
cs.AI,
cs.LG
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
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 is to support architectural decisions, providing a guideline for the inception and growth of these systems. This paper brings, side-by-side, the architecture representation of two systems that can be used as case studies for creating the metrics-based architectural model: the SPIRA and the Ocean Guard MLES.
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