A Metrics-Oriented Architectural Model to Characterize Complexity on Machine Learning-Enabled Systems
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 3 of its 29 references that are in the map. First posted on arXiv 9 Jun 2025.
Builds on
3 of 29 references.
References · 3
- 2020A Software Engineering Perspective on Engineering Machine Learning Systems: State of the Art and Challengescited by 33
- 2022Operationalizing Machine Learning: An Interview Studycited by 25
- 2023Data Pipeline Quality: Influencing Factors, Root Causes of Data-related Issues, and Processing Problem Areas for Developerscited by 5
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Citing papers per year: 2025: 2.
2 papers.