Improving Industry 4.0 Readiness: Monolith Application Refactoring using Graph Attention Networks

Abstract

Industry 4.0 utilizes cyber-physical systems to bridge the technological gap for the implementation of smart manufacturing techniques. This encompasses the use of advanced technologies like artificial intelligence, cloud and edge computing, and augmented reality. Machines need to work in harmony in order to achieve enhanced speeds and productivity. This harmony can be effectuated via the synchronization among machines using APIs to modernize their legacy systems. In other words, the long-existing monolithic frameworks in factory environments must be refactored into microservices. Software systems can be naturally represented as graphs. Software entities and their dependencies can be portrayed as nodes and edges, respectively. So, the task of refactoring can be condensed into a graph based clustering task. A novel graph attention based network is proposed in this work, to detect outliers to delineate the top refactor candidates, as well as to recommend clusters of microservices. Industrial microservice benchmarks have been identified to validate our model. Results show that our graph attention network improves state-of-the-art performance when compared to existing graph representation based refactoring techniques.