troessner/reek

Code smell detector for Ruby

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Summary Information

Updated 3 minutes ago
Added to GitGenius on September 16th, 2026
Created on September 5th, 2008
Open Issues & Pull Requests: 56 (+0)
GitHub issues: Enabled
Number of forks: 283
Total Stargazers: 4,130 (+0)
Total Subscribers: 41 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 19.0 hours
Mean response time: 50.1 days
90th percentile: 20.4 days
Tracked items: 12

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Issue Activity (beta)

Open issues: 5
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 1,178 days
Stale 30+ days: 4
Stale 90+ days: 4

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

Top labels

  • defect (4)
  • discussion (2)
  • smell (1)
  • wontfix (1)

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Detailed Description

Reek is a code smell detector for Ruby that identifies patterns in source code that may indicate deeper design problems.

The tool analyzes Ruby code to surface potential issues before they become maintenance burdens. Rather than enforcing style rules, Reek focuses on structural and design smells—patterns like duplicate code, long methods, too many instance variables, and unclear naming that suggest the code needs refactoring. It works by parsing Ruby source files and applying a configurable set of smell detection rules, allowing developers to catch design problems early in the development cycle.

Reek suits teams working on Ruby projects where code quality and maintainability matter. It works best as part of a broader quality practice, integrated into CI/CD pipelines or run locally during development. The tool is particularly valuable for legacy codebases where identifying refactoring opportunities can be difficult, and for teams that want to establish shared standards around what constitutes problematic code structure. Developers should adopt it if they want automated detection of design issues beyond what linters typically catch, though it requires understanding what each smell means and deciding which detections are worth addressing in their specific context.

The project shows consistent maintenance with regular updates addressing bug fixes and improvements to smell detection logic. The codebase demonstrates active engagement with user-reported issues and a willingness to refine detection rules based on real-world feedback. Development activity indicates a stable tool that evolves incrementally rather than through major rewrites, suggesting the maintainers prioritize reliability and backward compatibility for existing users.