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In the evolving landscape of software architecture, the shift from monolithic structures to agile, scalable microservices has revolutionized cloud-native application development. However, the inherent dynamism of microservices can lead to the inadvertent creation of unnecessary microservices, introducing complexity and inefficiency. Moreover, with a lack of control mechanisms in evolution, systems can lead to what is known as architecture degradation. This research ventures into the emerging domain of microservice-to-microservice transformation, a concept focused on optimizing existing cloud-native systems. We experiment with a machine learning methodology initially designed for monolith-to-microservices migration, adapting it to the complex microservices landscape, with a specific focus on the train-ticket application (Zhou in Association for Computing Machinery, https://doi.org/10.1145/3183440.3194991 …