Monolith to Microservices: Representing Application Software through Heterogeneous Graph Neural Network

TL;DR

This paper introduces a novel graph neural network approach to facilitate the migration from monolithic to microservice architectures by representing software artifacts and their relationships.

Contribution

It proposes a heterogeneous graph neural network model that jointly represents software components and their relationships to improve microservice extraction.

Findings

Effective on different types of monoliths

Improves clustering of software artifacts

Facilitates monolith to microservice migration

Abstract

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 alternate 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.