On the Variability of Microservice Decompositions: A Data-Driven Analysis

The problem of migrating monolithic applications to microservices has become popular both in industry and academia, particularly when using automated tools to assist developers in the decomposition. While a variety of tools and techniques have been proposed, deciding which is the most appropriate decomposition for a given monolith is challenging because the selected technique can return alternative decompositions depending on how the parameters of that technique are configured. This issue has not received enough attention in the literature, and therefore, developers have to resort to their intuition or use the default parameters reported by the authors of the technique. To investigate this problem further, in this work we perform a study of the parameters and variability of the MicroMiner approach, assessing its parameter sensitivity when dealing with two monolithic applications from the literature. Based on a systematic, data-driven analysis of the landscape of possible decompositions, our results show that, depending on the monolithic application provided as input, certain parameters of MicroMiner have more or less importance on the characteristics of the generated decompositions. These findings provide initial guidelines for developers to configure MicroMiner, as well as other approaches, in order to obtain microservice decompositions with relatively low variability.