Over the past decade, many software enterprises have migrated from monolithic to microservice architectures to enhance scalability, maintainability, and performance. However, this transition presents significant challenges, requiring considerable development efforts, research, customization, and resource allocation over extended periods. Furthermore, the success of migration is not guaranteed, highlighting the complexities organizations face in modernizing their software systems. To address these challenges, this study introduces Mono2Micro, a comprehensive framework designed to automate the migration process while preserving structural integrity and optimizing service boundaries. The framework focuses on three core patterns: database patterns, service decomposition, and communication patterns. It leverages machine learning algorithms, including Random Forest and Louvain clustering, to analyze database query patterns along with static and dynamic database model analysis, which enables the identification of relationships between models, facilitating the systematic decomposition of microservices while ensuring efficient inter-service communication. To validate its effectiveness, Mono2Micro was applied to a student information system for faculty management, demonstrating its ability to streamline the migration process while maintaining functional integrity. The proposed framework offers a systematic and scalable solution for organizations and researchers seeking efficient migration from monolithic systems to microservices.
Hassan et al. (2025) studied this question.