Method for Automated Decomposition of Monolithic Software Systems Based on Graph Neural Networks

The purpose of this study is to improve the architectural quality of software systems with microservice architecture by investigating and improving methods for automated transition from monolithic architecture to microservice architecture. The study was performed using methods of static source code analysis, graph theory, algorithms for transforming graph structures (detecting strongly connected components, redundant and cyclic dependencies), cluster analysis algorithms, graph neural networks, as well as multi-criteria assessment of internal cluster consistency and inter-cluster connectivity. The results presented in the study are a comprehensive solution to the scientific problem of ensuring the automated transformation of monolithic software systems into a microservice architecture with controlled inter-service connectivity and a high level of internal consistency of architectural components.