Organizations migrating monolithic applications to microservice architectures often face significant challenges in both decomposition and refactoring phases. While the decomposition step has received considerable automation research, refactoring remains predominantly manual, creating bottlenecks in migration efforts and preventing runtime-based and a more realistic evaluation of decomposition techniques. We propose a fully automated refactoring methodology that complements existing decomposition approaches. Our technique implements an ID-based and DTO-based hybrid design for inter-service communication and leverages Large Language Models (LLMs) for decision making, code analysis and code generation. Taking a monolith's source code and decomposition plan as input, our approach identifies “API classes” that cross service boundaries, selects their appropriate target design among the ID and DTO based methods and then automatically generates the necessary communication components—API contracts, server-side endpoints, and client-side proxies. This approach balances the preservation of the monolith's workflow consistency through the ID-based design and minimizing the overhead and complexity of the cross-service interactions through the DTO-based design. A qualitative evaluation using three benchmark applications demonstrates our approach's feasibility and advantages over related work.

The microservices architectural style offers many advantages such as scalability, reusability and ease of maintainability. As such microservices has become a common architectural choice when developing new applications. Hence, to benefit from these advantages, monolithic applications need to be redesigned in order to migrate to a microservice based architecture. Due to the inherent complexity and high costs related to this process, it is crucial to automate this task. In this paper, we propose a method that can identify potential microservices from a given monolithic application. Our method takes as input the source code of the source application in order to measure the similarities and dependencies between all of the classes in the system using their interactions and the domain terminology employed within the code. These similarity values are then used with a variant of a density-based clustering algorithm to generate a hierarchical structure of the recommended microservices while identifying potential outlier classes. We provide an empirical evaluation of our approach through different experimental settings including a comparison with existing human-designed microservices and a comparison with 5 baselines. The results show that our method succeeds in generating microservices that are overall more cohesive and that have fewer interactions in-between them with up to 0.9 of precision score when compared to human-designed microservices.