The popularity of microservices architecture has been increasing considerably because of its capacity to alleviate monolithic architecture issues. Nonetheless, the migration of monolith systems to microservices is a complex task. This work aims to analyze and characterize the state of the art on migrating monolithic applications towards microservices (semi-) automatically using Artificial Intelligence (AI) techniques by applying a systematic mapping methodology. Results showed that clustering is the preferred IA technique to decompose a monolith, cited by 63% out of the 22 reviewed studies. Moreover, the most prevailing input type used in migration techniques was source code (36.4%).
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Saucedo et al. (2024) studied this question.
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