🤖 AI Summary
Machine Learning–Enabled Systems (MLES) suffer from poorly quantifiable and unmanageable complexity, hindering systematic governance. Method: This paper proposes the first measurement-driven architectural model for MLES, innovatively extending the ML system reference architecture to natively support automated collection, cross-dimensional correlation analysis, and visualization of multi-faceted complexity indicators—including training data drift, model iteration coupling, and deployment heterogeneity. The approach integrates architectural modeling, software metrics engineering, and complexity quantification theory to shift complexity assessment from qualitative description to quantitative, traceable decision-making. Contribution/Results: Empirical evaluation demonstrates that the model significantly enhances complexity awareness and decision traceability during architectural evolution. It provides both theoretical foundations and practical tooling for sustainable MLES governance, enabling rigorous, evidence-based architectural management throughout the ML lifecycle.
📝 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.