An Event-Driven Tool for Context-Aware Code Smell Detection Using SmellDSL

TL;DR

SmellHunter is an event-driven, context-aware tool that enhances code smell detection by integrating static metrics with contextual information, aiding developers in refactoring decisions within Eclipse.

Contribution

It introduces SmellHunter, a novel, scalable, event-driven architecture that combines static code analysis with contextual data for improved code smell detection and developer support.

Findings

Integrates static metrics with contextual data for richer analysis.

Provides a scalable, event-driven architecture for code smell detection.

Supports developers with actionable insights via Eclipse and mobile app.

Abstract

Code smells signal violations of design principles that degrade the internal quality of evolving software systems. Although many tools detect such anomalies using static metrics, they often ignore the development context in which smells arise and are resolved. This limitation can lead to misleading warnings and weak support for refactoring decisions. To address this problem, we present SmellHunter, a context-aware tool that interprets scripts written in the SmellDSL domain-specific language to detect and contextualize code smells. SmellHunter integrates static code metrics with contextual information (such as team characteristics, project stage, and geographic metadata) to produce richer, more actionable analyses. The tool adopts an event-driven architecture in which a service bus orchestrates validation, interpretation, and persistence services through asynchronous events. This architecture enables scalable analysis while minimizing disruption to developers' workflows. SmellHunter is integrated into the Eclipse development environment via a dedicated plugin and provides aggregated insights via a mobile application, allowing developers to explore smell occurrences by type, severity, and location. By linking smell detection with contextual data and collaborative visualization, SmellHunter supports developers acting as smell hunters, helping teams identify recurring quality issues emerging from a particular location and assign refactoring tasks to developers with relevant expertise. We describe the architecture of SmellHunter, the interpretation process of SmellDSL scripts, and the integration of contextual data to support more informed refactoring decisions in modern software development environments.