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

Renato Cordeiro Ferreira
6/9/2025
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 showcases the first step for creating the metrics-based architectural model: an extension of a reference architecture that can describe MLES to collect their metrics.

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Cite this paper

@article{ferreira2025metricsoriented,
  title  = {A Metrics-Oriented Architectural Model to Characterize Complexity on Machine Learning-Enabled Systems},
  author = {Renato Cordeiro Ferreira},
  year   = {2025},
  eprint = {2506.08153},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2506.08153v1}
}

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