Automated assessment of the relationship between microservice architectures and performance

• We introduced a fully automated framework that quantifies the relationship between microservice architecture complexity, derived from multiple types of statically detected dependencies, and performance-related quality attributes. • We analyzed five complex benchmark systems, including four structurally distinct releases of the Train-Ticket microservice benchmark and one instance of the DeathStarBench suite. • Regarding the relationship between microservice-architecture complexity and overall performance, our analysis demonstrated that systems with poor structural complexity, reflected in high propagation cost and high Clique ratios, exhibited reduced scalability and supported fewer user requests. • Regarding the relationship between individual service complexity and their performance, our results show that endpoints with higher coupling scores tend to have longer response times. Using Spearman’s rank correlation to assess the correlation between each endpoint’s coupling score and its performance score, we find a statistically significant positive correlation. A microservice architecture is intended to promote modularity and evolvability. In this paper, we present an automated framework for assessing the relationship between microservice architecture complexity and performance-related quality attributes. In this framework, we use PPTAM, a performance testing tool, to evaluate system response time under varying user loads, and DV8, an architecture analysis tool, to assess architectural complexity and the complexity of individual services using coupling scores, propagation cost, and architectural antipatterns derived from various types of dependency relations. Using this approach, we evaluated five benchmark systems, including four releases of a microservice system that share similar functionalities but differ in structural design. The results show that microservice architectures with poor complexity scores also exhibited degraded performance outcomes. This automated framework, for the first time, enables a comprehensive measurement of microservice architecture complexity, formed through multiple types of statically extracted dependencies, and its correlation with dynamically obtained performance metrics.