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

2025/06/09 by Ferreira, Renato Cordeiro · 2 citations
#Artificial Intelligence (cs.AI) #D.2.11 #D.2.8 #FOS: Computer and information sciences #I.2.0 #Machine Learning (cs.LG) #Software Engineering (cs.SE)

paper · doi:10.48550/arxiv.2506.08153

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.

Citations

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