Year: 2026
Type: preprint
Abstract: Microservices have become a mainstream architectural paradigm, yet microservice bad smells can significantly harm maintainability and performance. Existing detection tools often produce obscure outputs and lack effective integration with runtime observability, making it difficult for operators to interpret results and take timely action. To address this gap, we propose SmellDoc, a customized framework based on Elastic Stack. SmellDoc extends the native observability dashboard with a microservice bad smell detection plugin, integrating detection, knowledge, and health monitoring. It introduces a Custom-Business-Collector to capture business-level metrics, a Re-integration Collector to aggregate heterogeneous runtime data, and detection components that combine static and runtime analyses. SmellDoc supports a knowledge base of 84 smell types and enables detection of 24 representative smells across architectural, runtime, and performance categories. Results are visualized in Kibana through multiple views, providing operators with actionable insights. Case studies on a benchmark microservice system demonstrate that SmellDoc is effective and usable in detecting, visualizing, and analyzing smells, thus enhancing runtime observability and accelerating troubleshooting to maintain a high level of Quality of Service.
Source: arXiv (Cornell University)
Institution: Harbin Institute of Technology
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7162398060
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