Facilitating the Migration to the Microservice Architecture via Model-Driven Reverse Engineering and Reinforcement Learning - Archive ouverte HAL Access content directly
Journal Articles Software and Systems Modeling Year : 2022

Facilitating the Migration to the Microservice Architecture via Model-Driven Reverse Engineering and Reinforcement Learning

Abstract

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 towards a set of microservices.
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Dates and versions

hal-03916547 , version 1 (30-12-2022)

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Mohammadhadi Dehghani, Shekoufeh Kolahdouz-Rahimi, Massimo Tisi, Dalila Tamzalit. Facilitating the Migration to the Microservice Architecture via Model-Driven Reverse Engineering and Reinforcement Learning. Software and Systems Modeling, 2022, 21 (3), pp.1115-1133. ⟨10.1007/s10270-022-00977-3⟩. ⟨hal-03916547⟩
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