How Far Is Machine Learning From the Detection of Complex Microservice Bad Smells?

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.