codeprobe-code-smells

Code smells like Long Method, Large Class, and Data Clumps are structural design issues in object-oriented code that hurt maintainability and readability. Identifying these anti-patterns early helps development teams reduce technical debt and improve long-term software quality.

Yes, static analysis for code smells applies to object-oriented languages such as Java, Python, and C#. It works across projects of varying sizes, from microservices to monoliths, automatically excluding generated or third-party code to focus on your source.

Configurable thresholds allow you to customize detection sensitivity for smells like Long Method and Large Class. By adjusting these limits, you control what constitutes a code smell in your specific project, ensuring the reported severity and suggested fixes are relevant to your refactoring goals.

Yes, the code smell detector excludes generated and third-party code from analysis. This ensures that the reported findings, locations, and suggested fixes focus strictly on your own source code, preventing false positives from external libraries.

Common anti-patterns and code smells to target during refactoring include Bloaters like Long Method and Primitive Obsession, Change Preventers like Shotgun Surgery, Couplers, and readability issues like Magic Numbers and Deep Nesting. Surfacing these specific anti-patterns guides targeted refactoring to improve overall code maintainability.

The best way to surface actionable code-smell findings in a monolith is to run an automated static analysis detector. It processes large codebases, excludes third-party code, and reports anti-patterns like Divergent Change with specific locations and suggested fixes to guide targeted refactoring.