Monolith to Microservices: Representing Application Software through Heterogeneous GNN

• URL: http://arxiv.org/abs/2112.01317v1
• Date: Wed, 1 Dec 2021 15:27:45 GMT
• Title: Monolith to Microservices: Representing Application Software through Heterogeneous GNN
• Authors: Alex Mathai, Sambaran Bandyopadhyay, Utkarsh Desai, Srikanth Tamilselvam
• Abstract summary: Microservice architectures are increasingly used as they advocate building an application through multiple smaller sized, loosely coupled functional services. This approach has made programming architecture as the natural choice for cloud based applications. But the challenges in the automated separation of functional modules for the already written monolith code slows down their migration task. We deduce this traditional application problem to a heterogeneous graph based clustering task. Our solution is the first of its kind to leverage heterogeneous graph neural network to learn representations of such diverse software entities.
• Score: 11.381828510219897
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Monolith software applications encapsulate all functional capabilities into a single deployable unit. While there is an intention to maintain clean separation of functionalities even within the monolith, they tend to get compromised with the growing demand for new functionalities, changing team members, tough timelines, non-availability of skill sets, etc. As such applications age, they become hard to understand and maintain. Therefore, microservice architectures are increasingly used as they advocate building an application through multiple smaller sized, loosely coupled functional services, wherein each service owns a single functional responsibility. This approach has made microservices architecture as the natural choice for cloud based applications. But the challenges in the automated separation of functional modules for the already written monolith code slows down their migration task. Graphs are a natural choice to represent software applications. Various software artifacts like programs, tables and files become nodes in the graph and the different relationships they share, such as function calls, inheritance, resource(tables, files) access types (Create, Read, Update, Delete) can be represented as links in the graph. We therefore deduce this traditional application decomposition problem to a heterogeneous graph based clustering task. Our solution is the first of its kind to leverage heterogeneous graph neural network to learn representations of such diverse software entities and their relationships for the clustering task. We study the effectiveness by comparing with works from both software engineering and existing graph representation based techniques. We experiment with applications written in an object oriented language like Java and a procedural language like COBOL and show that our work is applicable across different programming paradigms.

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