MicroMatic: Fully Automated Microservices Identification Approach From Monolithic Systems
Imen Trabelsi, Bianca Popa, Jeremie Pereyrol, Pier-Olivier Beaulieu, Naouel Moha · 2024
The Internet of Things (IoT) revolution is transforming system interactions and functionalities, necessitating more adaptable, scalable, and responsive systems architectures. These IoT systems build on recent advances in software architectures, particularly Microservices Architecture (MSA), enabling scalability, facilitating cloud deployment, and supporting seamless integration with DevOps practices. While new IoT applications can seamlessly integrate Microservices Architecture from their design, the migration of existing monolithic IoT systems to MSA is essential to leverage its benefits yet it remains a challenging and costly process. To facilitate this migration, we propose MicroMatic, a tooled fully automated microservices identification approach that is based on static-relationship analyses between code elements as well as semantic analyses of the source code. Our approach relies on Machine Learning (ML) techniques and uses service types to guide the identification of microservices from IoT monolithic systems. We validate the effectiveness of our tool through a detailed case study, comparing our results with established ground truths. This process included a quantitative evaluation of the microservices generated, focusing on their business capabilities. Our findings demonstrate the efficiency of MicroMatic in automating one of the most labour-intensive aspects of migrating legacy systems to a microservices framework, successfully identifying architecturally significant microservices with 62.5% precision and 45.5% recall.