Monolithic software encapsulates all functional capabilities into a single deployable unit. But managing it becomes harder as the demand for new functionalities grow. Microservice architecture is seen as an alternative as it advocates building an application through a set of loosely coupled small services wherein each service owns a single functional responsibility. But the challenges associated with the separation of functional modules, slows down the migration of a monolithic code into microservices. In this work, we propose a representation learning based solution to tackle this problem. We use a heterogeneous graph to jointly represent software artifacts (like programs and resources) and the different relationships they share (function calls, inheritance, etc.), and perform a constraint-based clustering through a novel heterogeneous graph neural network. Experimental studies show that our approach is effective on monoliths of different types.

Microservices are becoming the defacto design choice for software architecture. It involves partitioning the software components into finer modules such that the development can happen independently. It also provides natural benefits when deployed on the cloud since resources can be allocated dynamically to necessary components based on demand. Therefore, enterprises as part of their journey to cloud, are increasingly looking to refactor their monolith application into one or more candidate microservices; wherein each service contains a group of software entities (e.g., classes) that are responsible for a common functionality. Graphs are a natural choice to represent a software system. Each software entity can be represented as nodes and its dependencies with other entities as links. Therefore, this problem of refactoring can be viewed as a graph based clustering task. In this work, we propose a novel method to adapt the recent advancements in graph neural networks in the context of code to better understand the software and apply them in the clustering task. In that process, we also identify the outliers in the graph which can be directly mapped to top refactor candidates in the software. Our solution is able to improve state-of-the-art performance compared to works from both software engineering and existing graph representation based techniques.

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.