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Structural Decoupling and The Evolutionary Transition of Enterprise Systems: A Taxonomy of Microservice Extraction, Machine Learning-Assisted Boundary Detection, And Architectural Longevity

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Dr. Marcus Thorne 1 ,
4 Institute for Software Systems Engineering, University of Zurich, Switzerland

Abstract

The shift from monolithic architectures to microservices represents a fundamental change in the management of software complexity, scalability, and organizational alignment. This research provides an exhaustive analysis of the mechanisms governing this transition, focusing on the theoretical underpinnings of service extraction and the maintenance of long-lived software architectures. We investigate the persistence of the monolithic model, arguing that its continued relevance is predicated on specific economic and operational advantages that are often overlooked in the rush toward decentralization. Central to this study is the revisit of Conway’s Law, where we advocate for a task-based perspective on team-service alignment. The article further explores the extraction of microservices from legacy systems through a variety of techniques, including traditional refactoring and contemporary machine learning-assisted boundary detection. By establishing a comprehensive taxonomy of microservice anti-patterns, this work provides architects with a framework to avoid the common pitfalls of granularization. The methodology examines the optimization of microservice economics through granularity planning and the technical debt implications of architectural decay. Our findings suggest that successful modularization requires a symbiotic relationship between code-level refactoring and organizational task coordination, facilitated by automated identification tools that bridge the gap between abstract enterprise requirements and concrete service implementation.

Keywords

Microservices, Monolithic Architecture, Conway’s Law

References

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How to Cite

Dr. Marcus Thorne. (2025). Structural Decoupling and The Evolutionary Transition of Enterprise Systems: A Taxonomy of Microservice Extraction, Machine Learning-Assisted Boundary Detection, And Architectural Longevity. Global Multidisciplinary Journal, 4(12), 128-133. https://www.grpublishing.org/journals/index.php/gmj/article/view/336

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