A Tale of Two Systems: Characterizing Architectural Complexity on Machine Learning-Enabled Systems

Renato Cordeiro Ferreira
6/12/2025
cs.SEcs.AIcs.LG

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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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Cite this paper

@article{ferreira2025tale,
  title  = {A Tale of Two Systems: Characterizing Architectural Complexity on Machine Learning-Enabled Systems},
  author = {Renato Cordeiro Ferreira},
  year   = {2025},
  eprint = {2506.11295},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2506.11295v1}
}

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