How to ignore false positives in Vulture?

Vulture offers multiple methods: use whitelist files (recommended), add # noqa comments for flake8 compatibility (F401/F841), or use --ignore-names and --ignore-decorators flags. Whitelists can be auto-generated with --make-whitelist but often require manual tweaking to ensure correctness.

Vulture vs pyflakes for unused code detection?

Vulture specializes in dead code detection including unreachable code and provides confidence scores, while pyflakes catches broader errors but only reports unused imports and variables. Vulture is better for deep cleanup, but pyflakes is faster for general linting.

Can Vulture detect dead code in Flask or Django projects?

Yes, but it may struggle with dynamic routing or ORM usage. Use --ignore-decorators for Flask routes (e.g., @app.route) to reduce false positives, and whitelists are essential for handling framework-specific code patterns.

How to set up Vulture with pre-commit hooks?

Install pre-commit and add a configuration to .pre-commit-config.yaml specifying the Vulture repo and hook ID. Then, define paths and settings in pyproject.toml under tool.vulture to control which files are checked during commits.

What's the difference between Vulture and coverage.py for finding dead code?

Vulture uses static AST analysis without running code, so it's fast but prone to false positives. Coverage.py requires code execution and tests to identify unused lines, making it more accurate but slower and dependent on test coverage.

How to generate a whitelist automatically with Vulture?

Run vulture with --make-whitelist to output a whitelist Python file, then include it in subsequent scans. However, the auto-generated file may need manual edits to fix syntax or include missing imports for it to be runnable.

vulture — Python Dead Code Finder

What is vulture?

Vulture is a static analysis tool that finds unused (dead) code in Python programs, including functions, classes, variables, imports, and unreachable code. It helps developers clean up large codebases, identify untested code, and improve overall code quality by highlighting what can be safely removed. The tool uses abstract syntax tree (AST) analysis to provide fast results without executing the code.

Target Audience

Python developers working on medium to large codebases who want to maintain clean, efficient code and identify untested or obsolete code paths. It's also useful for teams integrating code quality checks into CI/CD pipelines or pre-commit hooks.

Value Proposition

Vulture offers a fast, static approach to dead code detection with configurable confidence scoring and whitelist support to handle false positives. Unlike coverage-based tools, it doesn't require code execution, making it easy to integrate into development workflows. Its compatibility with pyflakes output and extensive configuration options make it a practical choice for ongoing code maintenance.

Find dead Python code

Use Cases

Best For

Identifying unused imports and variables in Python projects
Cleaning up legacy codebases with large amounts of dead code
Finding untested functions by analyzing both source and test files
Detecting unreachable code blocks after control flow statements
Integrating dead code checks into pre-commit or CI/CD pipelines
Prioritizing code removal by sorting dead code by size (line count)

Not Ideal For

Projects with heavy metaprogramming or dynamic code execution (e.g., using eval() or plugin systems)
Teams needing 100% accurate dead code detection without any false positives or manual review
Small, single-file scripts where dead code is minimal and setup overhead outweighs benefits

Pros & Cons

Pros

Fast Static Analysis

Uses Python's ast module for syntax-based detection without code execution, making it quick and efficient for scanning large codebases, as noted in the README.

Configurable Confidence Scoring

Assigns confidence values (60-100%) to dead code, with a --min-confidence flag to filter results, allowing developers to focus on high-certainty issues first.

Whitelist Support for Accuracy

Supports generating or using whitelist files to suppress false positives, with auto-generation via --make-whitelist, improving precision over time.

Comprehensive Integration

Includes pre-commit hooks, GitHub Actions, a VS Code extension, and a programmatic API, making it easy to embed into CI/CD pipelines and development workflows.

Unreachable Code Detection

Identifies dead code after control flow statements like return or break, and unsatisfiable conditions, helping catch logical dead ends that other tools might miss.

Cons

High False Positive Rate

Due to Python's dynamic nature, Vulture often reports false positives or misses dead code, requiring manual whitelist management, as admitted in the README.

Complex Whitelist Management

Creating and maintaining whitelists can be tedious; auto-generated whitelists may need manual modifications to be syntactically correct, adding overhead.

Limited to Static Analysis

Cannot detect code used dynamically (e.g., via reflection or decorators), making it less reliable than execution-based tools like coverage for certain code paths.

Frequently Asked Questions

What is vulture?

Target Audience

Value Proposition

Use Cases

Best For

Identifying unused imports and variables in Python projects
Cleaning up legacy codebases with large amounts of dead code
Finding untested functions by analyzing both source and test files
Detecting unreachable code blocks after control flow statements
Integrating dead code checks into pre-commit or CI/CD pipelines
Prioritizing code removal by sorting dead code by size (line count)

Not Ideal For

Projects with heavy metaprogramming or dynamic code execution (e.g., using eval() or plugin systems)
Teams needing 100% accurate dead code detection without any false positives or manual review
Small, single-file scripts where dead code is minimal and setup overhead outweighs benefits

Pros & Cons

Pros

Fast Static Analysis

Uses Python's ast module for syntax-based detection without code execution, making it quick and efficient for scanning large codebases, as noted in the README.

Configurable Confidence Scoring

Assigns confidence values (60-100%) to dead code, with a --min-confidence flag to filter results, allowing developers to focus on high-certainty issues first.

Whitelist Support for Accuracy

Supports generating or using whitelist files to suppress false positives, with auto-generation via --make-whitelist, improving precision over time.

Comprehensive Integration

Includes pre-commit hooks, GitHub Actions, a VS Code extension, and a programmatic API, making it easy to embed into CI/CD pipelines and development workflows.

Unreachable Code Detection

Identifies dead code after control flow statements like return or break, and unsatisfiable conditions, helping catch logical dead ends that other tools might miss.

Cons

High False Positive Rate

Due to Python's dynamic nature, Vulture often reports false positives or misses dead code, requiring manual whitelist management, as admitted in the README.

Complex Whitelist Management

Creating and maintaining whitelists can be tedious; auto-generated whitelists may need manual modifications to be syntactically correct, adding overhead.

Limited to Static Analysis

Cannot detect code used dynamically (e.g., via reflection or decorators), making it less reliable than execution-based tools like coverage for certain code paths.

Frequently Asked Questions

vulture

What is vulture?

Overview

Use Cases

Best For

Not Ideal For

Pros & Cons

Pros

Cons

Frequently Asked Questions

Related Projects

Found a gem we're missing?

vulture

What is vulture?

Overview

Use Cases

Best For

Not Ideal For

Pros & Cons

Pros

Cons

Frequently Asked Questions

Related Projects

Found a gem we're missing?