ASDMG: business topic clustering-based architecture smell detection for microservice granularity

Abstract

Microservices architecture smells can significantly affect the quality of microservices due to poor design decisions, especially the granularity smells of microservice architectures will greatly affect the quality of a microservices architecture. The state-of-the-art methods of microservice architectural granularity detection primarily focus on the service level, which lacks consideration of detailed information such as interfaces, and these methods also lack considerations about semantic information related to business logic, leading to lower accuracy in the detection results. To address these issues, we introduce ASDMG, which takes semantic information within the Abstract Syntax Tree (AST) into consideration, integrating them with data dependency to extract business topic relationships of functions. It performs interface-oriented business topic clustering, allowing comprehensive detection of granularity smells both within individual microservices as well as the overall microservice architecture. Experiments were conducted using 5 open-source microservice systems in different scales and domains. Results show that ASDMG achieves an average precision of 83.41%, an average recall of 95.84%, and an average accuracy of 95.85% in detecting architectural granularity smells. Compared to state-of-the-art methods, it achieves better detection results and can improve the quality of microservice architecture.