Monolith to Microservice Candidates using Business Functionality Inference

In this paper, we propose a novel approach for monolith decomposition, that maps the implementation structure of a monolith application to a functional structure that in turn can be mapped to business functionality. First, we infer the classes in the monolith application that are distinctively representative of the business functionality in the application domain. This is done using formal concept analysis on statically determined code flow structures in a completely automated manner. Then, we apply a clustering technique, guided by the inferred representatives, on the classes belonging to the monolith to group them into different types of partitions, mainly: 1) functional groups representing microservice candidates, 2) a utility class group, and 3) a group of classes that require significant refactoring to enable a clean microservice architecture. This results in microservice candidates that are naturally aligned with the different business functions exposed by the application. A detailed evaluation on four publicly available applications show that our approach is able to determine better quality microservice candidates when compared to other existing state of the art techniques. We also conclusively show that clustering quality metrics like modularity are not reliable indicators of microservice candidate goodness.