ABSTRACT Microservice bad smells (MBSs) affect system quality. However, the lack of comprehensive and detailed explanations of MBSs and current classification methods does not comprehensively encompass the microservice characteristics, making it challenging to study and address MBSs. Existing studies have focused on MBS detection methods, but identifying and quantifying the effects of different smells and properly assessing system health remain challenging. This review aims to provide an exhaustive list of the MBSs and formulate a reasonable classification based on microservice characteristics. By reasonably assessing the harmfulness of smells, we would comprehensively explain different MBSs. We would explore microservice system evaluation methods and propose a microservice system health assessment model (MSHAM) based on MBSs. We conducted a systematic literature review (SLR) of the catalog and evaluation literature in the field of MBSs. We also combined the open questionnaire form to answer the research questions qualitatively and quantitatively. This paper presents a comprehensive list of 69 types of MBSs, incorporating a highly scalable classification. Following the quality characteristics in ISO/IEC 25010:2023, we determined the sets of characteristics affected by different smells, and provided detailed explanations of smells in the form of MBSs knowledge base. Furthermore, we introduced the MSHAM, revealing the quantification process of harmfulness. The formulated smells list and classification method capture MBS characteristics comprehensively, ensuring scalability. We have exposed the impact of MBSs, enhancing researcher and practitioner understanding. MSHAM provides a logical model to quantify MBSs harm and system health, supporting the development of real‐time health monitoring.

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.

ABSTRACT Microservice bad smells, arising from poor design and development practices, can severely degrade system quality if unaddressed. While rule‐based detection methods exist, their applicability is limited by subjective metric thresholds and the difficulty in defining certain bad smells, particularly complex microservice bad smells that are challenging to express through rules or involve high subjectivity. These smells often involve multiple services or manifest across multiple layers within a service, making them particularly challenging to detect using traditional methods. Without efficient and accurate detection mechanisms, the self‐healing capabilities of microservices during operation and continuous evolution will also be compromised. Given the promise of machine learning in code smell detection, this study empirically evaluates its performance in detecting complex microservice bad smells. We employ two sampling techniques and eight classification models on 1180 samples from 55 systems, generating 45 detection models and identifying top classifiers for seven complex microservice bad smell types. We compare machine learning with rule‐based methods for high‐subjectivity smells, analyze performance gaps, and propose a MAPE‐K‐based conceptual framework for runtime detection and refactoring. Finally, we discuss the necessity for future research.