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

TL;DR

This paper proposes a metrics-based architectural model to quantify and manage the complexity of machine learning-enabled systems, aiding architectural decisions and system growth.

Contribution

It introduces an extension of reference architecture to describe MLES and collect relevant metrics for complexity characterization.

Findings

Extended reference architecture for MLES

Framework for collecting complexity metrics

Supports architectural decision-making

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.