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.

Live testing is performed in the production environment. In such environment, test activities have to be orchestrated properly to avoid interferences with normal usage traffic. Conducting live testing activities manually is error prone because of the size and the complexity of the system as well as the required complex orchestration of different tasks. Furthermore, it would be impossible to react to failures and contain them in due time without automation. Live testing requires a high level of automation. This automation comes with several challenges especially in contexts such as cloud and zero touch networks because of the diversity of the software composing them. In this paper we discuss the challenges of automating live testing for cloud systems. We propose an architecture that relies on a modeling framework to decouple the specification of testing activities from the platforms needed to conduct them. We propose a solution for conducting testing activities on a live system according to such a specification.

Configurations play an important role in the behavior and operation of configurable systems. Prior to deployment a configured system is tested in the development environment. However, because of the differences between the development environment and the production environment, the configuration of the system needs to be adapted for the production environment. It is therefore important to re-test the configured system in the production environment. Since the system has already been tested in the development environment one should avoid reapplying all the test cases, it is desirable to reduce the test suite to be used in the production environment as much as possible. This is the goal of the method we propose in this paper. For this, we explore the similarities between the configuration used in the development environment and the configuration for the production environment to eliminate test cases. Indeed, the difference between the two configurations is only at the environment level, i.e. only the configuration parameters that influence the interactions between the system and its environment are changed for the deployment in the production environment. We propose a method that is based on a classification of the configuration parameters (based on their dependency to the environment) and use it to reduce the development time test suite before reapplying it in the production environment.