Systematic Service Boundary Identification and Evolutionary Refactoring of Legacy Monoliths into Resilient Microservice Architectures: A Domain-Driven and Machine Learning-Assisted Approach - Dr. Matthias Schneider (Emerging Frontiers Library for The American Journal of Engineering and Technology, 2023)
Background: The migration from monolithic systems to microservice architectures represents one of the most significant paradigm shifts in contemporary software engineering. While microservices promise scalability, resilience, and organizational alignment, the transformation of legacy monoliths remains complex, risk-laden, and methodologically fragmented. Foundational works on domain-driven design, refactoring, legacy code management, service decomposition, and evolutionary migration provide partial guidance, yet an integrated, systematic framework for service boundary identification and transformation remains underdeveloped. Objective: This study proposes a comprehensive, publication-ready framework that integrates domain-driven design principles, systematic service decomposition methods, workload-based clustering, topic modeling, architectural meta-modeling, evolutionary refactoring strategies, and machine learning-assisted service boundary detection. The objective is to synthesize established theoretical foundations into a coherent transformation methodology suitable for industrial adoption. Methods: Drawing strictly from established literature, the research develops a multi-phase transformation framework. The approach incorporates domain analysis, bounded context identification, service cutter decomposition, workload-based clustering, topic modeling of source code artifacts, architectural meta-modeling for granularity control, evolutionary migration patterns, resilience-oriented refactoring, and machine learning-assisted modularization. The framework is conceptually validated through theoretical alignment with prior empirical findings and architectural principles. Results: The proposed framework demonstrates theoretical improvements in service cohesion, reduced coupling, granularity optimization, resilience enhancement, and legacy risk mitigation. By combining human-driven domain modeling with data-driven boundary detection, the approach balances conceptual rigor and empirical grounding. Evolutionary migration strategies reduce transformation risk, while refactoring and legacy isolation techniques enhance maintainability. Conclusion: The study contributes a unified transformation methodology that bridges foundational architectural theory and contemporary machine learning techniques. It offers a structured pathway for organizations transitioning from monoliths to resilient microservice ecosystems while minimizing technical debt and architectural erosion.