Microservices is a popular architectural approach for complex systems in companies, despite its nature of decentralization. There is a comprehensive set of microservices architectural patterns that guides implementations and helps developers to overcome issues. However, the community still scarcely adopts these patterns and only has a theoretical understanding of them. In this work, in order to increase awareness of such patterns and provide aid to developers to better understand an architecture based on microservices, we propose a detection approach based on metrics for microservices patterns. We focused on structural or architectural patterns, and implemented detection for five of them. We conducted two case studies with real-world applications and evaluated the accuracy and applicability of our approach with the developers of those applications.

The promise of increased agility, autonomy, scalability, and reusability has made the microservices architecture a de facto standard for the development of large-scale and cloud-native commercial applications. Software patterns are an important design tool, and often they are selected and combined with the goal of obtaining a set of desired quality attributes. However, from a research standpoint, many patterns have not been widely validated against industry practice, making them not much more than interesting theories. To address this, we investigated how practitioners perceive the impact of 14 patterns on 7 quality attributes. Hence, we conducted 9 semi-structured interviews to collect industry expertise regarding (1) knowledge and adoption of software patterns, (2) the perceived architectural trade-offs of patterns, and (3) metrics professionals use to measure quality attributes. We found that many of the trade-offs reported in our study matched the documentation of each respective pattern, and identified several gains and pains which have not yet been reported, leading to novel insight about microservice patterns.