SmellDoc: Extending Elastic Stack for Microservice Bad Smell Detection and Visualization

2026-05-23

AI Summary

This work addresses the detrimental impact of microservice code smells on system maintainability and performance, exacerbated by existing detection tools that produce opaque results and lack integration with runtime observability infrastructures. To bridge this gap, the authors propose SmellDoc—a customized framework built upon the Elastic Stack that seamlessly integrates static code analysis, runtime monitoring, and a knowledge base encompassing 84 distinct smells through a dedicated Kibana plugin. SmellDoc enables cross-category detection and visualization of 24 representative microservice smells. Its key innovation lies in the introduction of two novel collectors: the Custom-Business-Collector and the Re-integration Collector, which effectively fuse business-level metrics with heterogeneous runtime telemetry data. Case studies demonstrate that SmellDoc substantially enhances runtime observability, accelerates fault localization, and ultimately safeguards service quality.

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.

Problem

• microservice bad smells
• observability
• detection tools
• runtime monitoring
• maintainability

Innovation

• microservice bad smells
• Elastic Stack
• runtime observability
• static and dynamic analysis
• Kibana visualization