• 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.

Complex software systems consist of multiple overlapping design structures, such as abstractions, features, crosscutting concerns, or patterns. This is similar to how a human body has multiple interacting subsystems, such as respiratory, digestive, or circulatory. Unlike in the medical domain, software designers do not have an effective way to distinguish, visualize, comprehend, and analyze these interleaving design structures. As a result, developers often struggle through the maze of source code. In this paper, we present anAutomated Concept Explanation(ACE) framework that automatically extracts and categorizes major concepts from source code based on the roles that files play in design structures and their topic frequencies. Based on these categorized concepts, ACE recovers four categories of high-level design models using different algorithms and generates a natural language explanation for each. To assess if and how ACE can help developers better understand design structures, we conducted an empirical study where two groups of graduate students were assigned three design comprehension tasks: identifying feature-related files, identifying dependencies among features, and identifying design patterns used, in an open-source project. The results reveal that the students who used ACE can accomplish these tasks much faster and more accurately, and they acknowledged the usefulness of the categorized concepts and structures, multi-type high-level model visualization, and natural language explanations.

Background: Software practitioners need reliable metrics to monitor software evolution, compare projects, and understand modularity variations. This is crucial for assessing architectural improvement or decay. Existing popular metrics offer little help, especially in systems with implicitly connected but seemingly isolated files.