Monolith to Microservices: Representing Application Software through Heterogeneous Graph Neural Network
This addresses a domain-specific problem for software engineers by providing an incremental improvement in automating software migration.
The paper tackles the challenge of migrating monolithic software to microservice architectures by proposing a representation learning solution using a heterogeneous graph neural network with constraint-based clustering, showing effectiveness across different types of monoliths in experiments.
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.