Microservice API Pattern Detection : Using Business Processes and Call Graphs

It is well recognized that design patterns improve system development and maintenance in many aspects. While we commonly recognize these patterns in monolithic systems, many patterns emerged for cloud computing, specifcally microservices. Unfortunately, while various patterns have been proposed, available quality assessment tools often do not recognize many. This thesis performs a grey literature review to fnd and catalog available tools to detect microservice API patterns. It reasons about mechanisms that can be used to detect these patterns. Furthermore, the results indicate gaps and opportunities for improvements for quality assessment tools. There are available tools commonly used by practitioners that offer centralized logging, tracing, and metric collection for microservices. We assess the opportunity to combine current dynamic analysis tools with anomaly detection in the form of patterns and anti-patterns. We develop a tool prototype that we use to assess a large microservice system benchmark demonstrating the feasibility and potential of such an approach