SABER: A MAPE-K-based Self-Adaptive Framework for Microservice Bad Smell Refactoring

Download PDF
Open Webpage

Yongchao Xing, Yiming Lv, Xianglin Zeng, Bohai Zhao, Kai Zhang, Hongliang Sun, Weipan Yang, Zhiying Tu

Published: 2025, Last Modified: 05 May 2026ICWS 2025EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: To address the limitations of existing microservice bad smell (MBS) detection and refactoring tools, particularly the lack of fully automated architectural bad smell refactoring solutions, this paper proposes a MAPE-K-based self-adaptive framework for microservice bad smell refactoring (SABER). The framework aims to eliminate architectural smells through closed-loop self-repair, thereby reducing risks related to main-tainability, scalability, and security. SABER employs a cloud-edge collaborative architecture: edge-side components collect real-time metrics from a Kubernetes cluster, while cloud-side components detect architectural smells and dynamically generate refactoring strategies. These strategies include service merging, splitting, adding, and adjustment. By automatically executing these strategies, SABER achieves architectural bad smell elimi-nation. Experimental results show that the framework achieves 95.53 % precision and 84.71 % recall across ten benchmark systems, significantly improving refactoring efficiency compared to semi-automated and manual methods. Its deep integration with DevOps pipelines validates its effectiveness in sustaining microservice health, offering a novel paradigm for autonomous maintenance in distributed systems.