An Innovated Microservices Identification Approach Based on Database and Source Code Analysis

Abstract

Migrating from Monolithic architecture to Microservices architecture is a major change in how applications are designed, developed, and managed. This paper introduces an innovative approach for Microservices identification. Advanced techniques, including the OpenAI ChatGPT API, BERT embeddings, and community detection algorithms, are used to analyze both the database and the source code to find the optimal service boundaries. Thorough evaluations on benchmark applications demonstrate that this proposed technique surpasses cutting-edge solutions in structural modularity and maintains favorable values for other software quality metrics. This aids in identifying scalable Microservices. This research not only supports the modernization of software infrastructure but also advocates for sustainable development practices within the ever-changing field of distributed systems.