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Facilitating the migration to the microservice architecture via model-driven reverse engineering and reinforcement learning

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The microservice architecture has gained remarkable attention in recent years. Microservices allow developers to implement and deploy independent services, so they are a naturally effective architecture for continuously deployed systems. Because of this, several organizations are undertaking the costly process of manually migrating their traditional software architectures to microservices. The research in this paper aims at facilitating the migration from monolithic software architectures to microservices. We propose a framework which enables software developers/architects to migrate their software systems more efficiently by helping them remodularize the source code of their systems. The framework leverages model-driven reverse engineering to obtain a model of the legacy system and reinforcement learning to propose a mapping of this model toward a set of microservices.

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  2. As we will see in the next section, the general approach depends on the number of MSs of the system. The closest general NNET in terms of the number of MSs can be used as a starting point for the individual approach training.



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Correspondence to Shekoufeh Kolahdouz-Rahimi.

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Communicated by L. Burgueño, J. Cabot, M. Wimmer & S. Zschaler.

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Dehghani, M., Kolahdouz-Rahimi, S., Tisi, M. et al. Facilitating the migration to the microservice architecture via model-driven reverse engineering and reinforcement learning. Softw Syst Model 21, 1115–1133 (2022).

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