code-smell-detector

Code smell detection involves analyzing your software project to identify patterns like long methods, duplicate code, large classes, and dead code. The tool provides illustrative examples and refactoring recommendations for each issue to reduce technical debt.

Common code smells include feature envy, primitive obsession, magic numbers, switch statement smells, and comment smells. These patterns indicate deeper maintainability problems in the source code and can be identified through static analysis to facilitate targeted refactoring.

Static analysis can automatically find magic numbers and dead code in your codebase. It reports on these and other code smells like deep nesting and duplicate code, providing illustrative examples and refactoring recommendations for each identified issue.

To get refactoring recommendations for large classes and long methods, run a code smell detector on your source files. It analyzes the code quality, identifies these structural issues, and suggests specific refactoring approaches to improve maintainability.

Code smell detection requires no specific dependencies or environment setup. You can directly analyze your source code files, such as a Python script, to identify various code smells and receive refactoring recommendations without additional configuration.

The best way to analyze code quality and reduce technical debt is to run a comprehensive code smell detector on your project files. It identifies maintainability issues like duplicate code and feature envy, providing prioritized refactoring recommendations based on severity.