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

• URL: http://arxiv.org/abs/2506.08153v1
• Date: Mon, 09 Jun 2025 19:02:19 GMT
• Title: A Metrics-Oriented Architectural Model to Characterize Complexity on Machine Learning-Enabled Systems
• Authors: Renato Cordeiro Ferreira,
• Abstract summary: This research aims to introduce a metrics-based architectural model to characterize the complexity of MLES.<n>The goal is to support architectural decisions, providing a guideline for the inception and growth of these systems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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