Centrality Change Proneness: an Early Indicator of Microservice Architectural Degradation

Abstract: Over the past decade, the wide adoption of Microservice Architecture has required the identification of various patterns and anti-patterns to prevent Microservice Architectural Degradation. Frequently, the systems are modelled as a network of connected services. Recently, the study of temporal networks has emerged as a way to describe and analyze evolving networks. Previous research has explored how software metrics such as size, complexity, and quality are related to microservice centrality in the architectural network. This study investigates whether temporal centrality metrics can provide insight into the early detection of architectural degradation by correlating or affecting software metrics. We reconstructed the architecture of 7 releases of an OSS microservice project with 42 services. For every service in every release, we computed the software and centrality metrics. From one of the latter, we derived a new metric, Centrality Change Proneness. We then explored the correlation between the metrics. We identified 7 size and 5 complexity metrics that have a consistent correlation with centrality, while Centrality Change Proneness did not affect the software metrics, thus providing yet another perspective and an early indicator of microservice architectural degradation.