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Microservices are becoming the defacto design choice for softwarearchitecture. It involves partitioning the software components into finermodules such that the development can happen independently. It also providesnatural benefits when deployed on the cloud since resources can be allocateddynamically to necessary components based on demand. Therefore, enterprises aspart of their journey to cloud, are increasingly looking to refactor theirmonolith application into one or more candidate microservices; wherein eachservice contains a group of software entities (e.g., classes) that areresponsible for a common functionality. Graphs are a natural choice torepresent a software system. Each software entity can be represented as nodesand its dependencies with other entities as links. Therefore, this problem ofrefactoring can be viewed as a graph based clustering task. In this work, wepropose a novel method to adapt the recent advancements in graph neuralnetworks in the context of code to better understand the software and applythem in the clustering task. In that process, we also identify the outliers inthe graph which can be directly mapped to top refactor candidates in thesoftware. Our solution is able to improve state-of-the-art performance comparedto works from both software engineering and existing graph representation basedtechniques.

Graph Neural Network to Dilute Outliers for Refactoring Monolith Application