Vulture and deadcode - Dead Code Detection

Tools for finding unused Python code including functions, classes, variables, imports, and attributes.

Overview

Vulture (mature, confidence-based) and deadcode (newer, AST-based) both detect unused code but with different approaches:

Feature	Vulture	deadcode
Approach	Static analysis + confidence scores	AST-based detection
Accuracy	Confidence scores (60-100%)	High accuracy, fewer false positives
Speed	Fast	Very fast
Configuration	Whitelist files	TOML configuration
Maturity	Mature (2012)	Newer (2023+)
Best For	Large codebases, gradual cleanup	New projects, strict enforcement

Installation

bash

# Install vulture
uv add --dev vulture

# Install deadcode (newer alternative)
uv add --dev deadcode

# Install both for comparison
uv add --dev vulture deadcode

Vulture - Confidence-Based Detection

Basic Usage

bash

# Check entire project
vulture .

# Check specific files/directories
vulture src/ tests/

# Minimum confidence threshold (60-100%)
vulture --min-confidence 80 .

# Exclude patterns
vulture . --exclude "**/migrations/*,**/tests/*"

# Sort by confidence
vulture --sort-by-size .

# Generate whitelist of current issues
vulture . --make-whitelist > vulture_whitelist.py

Configuration

pyproject.toml Configuration

toml

[tool.vulture]
# Minimum confidence to report (60-100%)
min_confidence = 80

# Paths to scan
paths = ["src", "tests"]

# Exclude patterns (glob)
exclude = [
    "**/migrations/*",
    "**/__pycache__/*",
    "**/node_modules/*",
    ".venv/*"
]

# Ignore decorators (marks functions as used)
ignore_decorators = [
    "@app.route",
    "@pytest.fixture",
    "@property",
    "@staticmethod",
    "@classmethod"
]

# Ignore names matching patterns
ignore_names = [
    "test_*",      # Test functions
    "setUp*",      # Test setup
    "tearDown*",   # Test teardown
]

# Make whitelist
make_whitelist = false

# Sort results
sort_by_size = false

vulture_whitelist.py Pattern

python

# vulture_whitelist.py
# Whitelist for false positives

# Used by external code
_.used_by_external_lib  # confidence: 60%
MyClass.called_dynamically  # confidence: 60%

# Used in templates
def render_template_helper():
    pass  # confidence: 60%

# Used via __getattr__
dynamic_attribute = None  # confidence: 60%

# Framework magic
class Meta:  # Django/Flask metadata
    pass

# Plugin system
def plugin_hook():  # Called by plugin system
    pass

Understanding Confidence Scores

python

# 100% confidence (definitely unused)
def never_called():
    """This function is never called anywhere."""
    pass

# 80% confidence (likely unused)
def maybe_called():
    """Called in commented code or string."""
    pass

# 60% confidence (possibly unused)
def dynamic_call():
    """Might be called via getattr() or string."""
    pass

# Set minimum confidence threshold
# --min-confidence 80 = Report only high-confidence issues
# --min-confidence 60 = Report all potential issues (more false positives)

Common Patterns

Unused Imports

python

# FOUND: Unused import
import sys  # confidence: 100%
import os   # confidence: 100%

# USED: Import is used
import logging
logger = logging.getLogger(__name__)

Unused Functions

python

# FOUND: Unused function
def unused_helper():  # confidence: 100%
    return 42

# USED: Function is called
def used_helper():
    return 42

result = used_helper()

Unused Class Attributes

python

class MyClass:
    # FOUND: Unused attribute
    unused_attr = 42  # confidence: 100%

    # USED: Attribute is accessed
    used_attr = 42

    def method(self):
        return self.used_attr

Unused Variables

python

def process():
    # FOUND: Unused variable
    unused = calculate()  # confidence: 100%

    # USED: Variable is used
    result = calculate()
    return result

False Positives (When to Whitelist)

python

# 1. Dynamic attribute access
class Config:
    DEBUG = True  # Accessed via getattr(config, 'DEBUG')

# 2. Framework magic
class Meta:  # Used by Django ORM
    db_table = 'users'

# 3. Decorators
@app.route('/api/data')
def api_endpoint():  # Route handler - appears unused
    pass

# 4. Test fixtures
@pytest.fixture
def sample_data():  # Fixture - appears unused
    return [1, 2, 3]

# 5. Plugin hooks
def plugin_initialize():  # Called by plugin system
    pass

# 6. Serialization
class User:
    def to_dict(self):  # Called by serialization library
        pass

Gradual Cleanup Strategy

bash

# Step 1: Generate baseline
vulture --make-whitelist > vulture_whitelist.py

# Step 2: Fix high-confidence issues
vulture --min-confidence 90 .

# Step 3: Lower threshold gradually
vulture --min-confidence 80 .
vulture --min-confidence 70 .

# Step 4: Update whitelist
vulture --make-whitelist > vulture_whitelist.py

# Step 5: Enforce in CI
vulture --min-confidence 80 .

deadcode - AST-Based Detection

Basic Usage

bash

# Check entire project
deadcode .

# Check specific files/directories
deadcode src/

# Verbose output
deadcode --verbose .

# Dry run (show what would be removed)
deadcode --dry-run .

# Show unreachable code
deadcode --show-unreachable .

# Generate configuration
deadcode --init

Configuration

pyproject.toml Configuration

toml

[tool.deadcode]
# Paths to scan
paths = ["src"]

# Exclude patterns
exclude = [
    "tests/*",
    "**/__pycache__/*",
    "**/migrations/*",
]

# Files to ignore completely
ignore_files = [
    "src/legacy.py",
    "src/experimental.py"
]

# Directories to exclude
exclude_dirs = [
    ".venv",
    "node_modules",
    ".git"
]

# Functions/classes to ignore
ignore_names = [
    "test_*",      # Test functions
    "setUp",       # Test methods
    "tearDown",
    "main",        # Entry points
]

# Ignore decorators
ignore_decorators = [
    "app.route",
    "pytest.fixture",
    "property",
    "staticmethod",
    "classmethod",
    "abstractmethod"
]

# Minimum number of references to consider "used"
min_references = 1

# Show unreachable code (after return/raise)
show_unreachable = false

Common Patterns

Unused Code Detection

python

# FOUND: Unused function
def unused_function():
    return 42

# FOUND: Unused class
class UnusedClass:
    pass

# FOUND: Unused variable
UNUSED_CONSTANT = 42

# FOUND: Unused import
import unused_module

Unreachable Code Detection

python

def example():
    return 42
    print("Unreachable")  # FOUND: Code after return

def example2():
    raise ValueError("Error")
    cleanup()  # FOUND: Code after raise

def example3():
    if True:
        return
    else:
        process()  # FOUND: Unreachable branch

False Positives (Configuration)

python

# 1. Public API (keep even if unused internally)
# Add to ignore_names in pyproject.toml
[tool.deadcode]
ignore_names = ["PublicAPIClass", "public_function"]

# 2. Framework magic
[tool.deadcode]
ignore_decorators = ["app.route", "celery.task"]

# 3. Test infrastructure
[tool.deadcode]
ignore_names = ["test_*", "setUp*", "tearDown*"]

# 4. Entry points
[tool.deadcode]
ignore_names = ["main", "__main__"]

Comparison: Vulture vs deadcode

When to Choose Vulture

Use Vulture for:

•Large, mature codebases with complex dynamics
•Gradual cleanup with confidence-based filtering
•Whitelisting false positives easily
•Handling dynamic code (getattr, exec, etc.)

Example workflow:

bash

# Start with high confidence
vulture --min-confidence 90 .

# Generate whitelist for false positives
vulture --make-whitelist > vulture_whitelist.py

# Edit whitelist manually
vim vulture_whitelist.py

# Run with whitelist
vulture . vulture_whitelist.py

When to Choose deadcode

Use deadcode for:

•New projects with strict code hygiene
•Fewer false positives desired
•AST-based accuracy
•Unreachable code detection

Example workflow:

bash

# Generate initial config
deadcode --init

# Configure in pyproject.toml
[tool.deadcode]
paths = ["src"]
ignore_decorators = ["app.route"]

# Run checks
deadcode .

# Enforce in CI
deadcode --strict .

Hybrid Approach

Use both tools for comprehensive detection:

bash

# Run vulture for broad detection
vulture --min-confidence 80 .

# Run deadcode for precise detection
deadcode .

# Compare results and whitelist false positives

CI Integration

GitHub Actions with Vulture

yaml

# .github/workflows/deadcode.yml
name: Dead Code Check

on: [push, pull_request]

jobs:
  vulture:
    runs-on: ubuntu-latest

    steps:
      - uses: actions/checkout@v4

      - name: Install uv
        uses: astral-sh/setup-uv@v2

      - name: Set up Python
        run: uv python install 3.12

      - name: Install dependencies
        run: uv sync --all-extras --dev

      - name: Run vulture
        run: |
          uv run vulture . \
            --min-confidence 80 \
            vulture_whitelist.py

      - name: Upload results
        uses: actions/upload-artifact@v4
        if: failure()
        with:
          name: vulture-results
          path: vulture-output.txt

GitHub Actions with deadcode

yaml

# .github/workflows/deadcode.yml
name: Dead Code Check

on: [push, pull_request]

jobs:
  deadcode:
    runs-on: ubuntu-latest

    steps:
      - uses: actions/checkout@v4

      - name: Install uv
        uses: astral-sh/setup-uv@v2

      - name: Set up Python
        run: uv python install 3.12

      - name: Install dependencies
        run: uv sync --all-extras --dev

      - name: Run deadcode
        run: uv run deadcode .

      - name: Check for unreachable code
        run: uv run deadcode --show-unreachable .

Pre-commit Hook

yaml

# .pre-commit-config.yaml
repos:
  # Vulture
  - repo: https://github.com/jendrikseipp/vulture
    rev: v2.11
    hooks:
      - id: vulture
        args: ['--min-confidence', '80']
        files: ^src/

  # deadcode
  - repo: https://github.com/albertas/deadcode
    rev: v2.0.0
    hooks:
      - id: deadcode
        args: ['.']
        files: ^src/

Best Practices

1. Start with High Confidence

bash

# Begin with high confidence (fewer false positives)
vulture --min-confidence 90 .

# Gradually lower threshold
vulture --min-confidence 80 .
vulture --min-confidence 70 .

2. Use Whitelists for False Positives

python

# vulture_whitelist.py
# Document WHY each item is whitelisted

# Framework routes (Flask/Django)
@app.route('/api/endpoint')
def api_handler():  # Called by framework
    pass

# Pytest fixtures
@pytest.fixture
def sample_data():  # Used by test functions
    return [1, 2, 3]

# Plugin hooks
def on_load():  # Called by plugin system
    pass

3. Integrate into Development Workflow

bash

# Local development: Quick check
vulture src/ --min-confidence 90

# Pre-commit: Catch obvious issues
pre-commit run vulture --all-files

# CI: Strict enforcement
vulture . --min-confidence 80 vulture_whitelist.py

4. Review and Clean Regularly

bash

# Weekly: Review dead code
vulture --make-whitelist > current_issues.txt

# Compare with last week
diff current_issues.txt last_week_issues.txt

# Clean up incrementally
git grep "def unused_function" | xargs -I {} git rm {}

5. Combine with Other Tools

bash

# Dead code + unused imports
vulture . && ruff check --select F401 .

# Dead code + type checking
vulture . && basedpyright

# Dead code + coverage
pytest --cov=src && vulture src/

Common Pitfalls

1. Dynamic Code

python

# Problem: vulture can't detect dynamic usage
def dynamic_call():
    pass

# Called via getattr
func = getattr(module, "dynamic_call")
func()

# Solution: Whitelist or use ignore comment
def dynamic_call():  # noqa: vulture
    pass

2. Test Code

python

# Problem: Test fixtures appear unused
@pytest.fixture
def sample_data():  # Appears unused to vulture
    return [1, 2, 3]

# Solution: Configure ignore_decorators
[tool.vulture]
ignore_decorators = ["pytest.fixture"]

3. Public API

python

# Problem: Public API unused internally
class PublicAPI:
    def public_method(self):  # Not called in codebase
        pass

# Solution: Whitelist or document
[tool.deadcode]
ignore_names = ["PublicAPI"]

4. Framework Magic

python

# Problem: Framework calls code dynamically
class Meta:  # Django ORM metadata
    db_table = 'users'

@app.route('/api')  # Flask route
def endpoint():
    pass

# Solution: Configure ignore_decorators
[tool.vulture]
ignore_decorators = ["app.route"]
ignore_names = ["Meta"]

Integration with IDEs

VS Code

json

// settings.json
{
  "python.linting.enabled": true,
  "python.linting.vulture.enabled": true,
  "python.linting.vulture.args": [
    "--min-confidence", "80"
  ]
}

Neovim with LSP

lua

-- Using null-ls
local null_ls = require("null-ls")

null_ls.setup({
  sources = {
    null_ls.builtins.diagnostics.vulture.with({
      extra_args = { "--min-confidence", "80" }
    }),
  }
})

Summary

Vulture provides confidence-based dead code detection:

•Install: uv add --dev vulture
•Usage: vulture --min-confidence 80 .
•Whitelists: vulture_whitelist.py for false positives
•Best for: Large codebases, gradual cleanup

deadcode provides AST-based detection:

•Install: uv add --dev deadcode
•Usage: deadcode .
•Configuration: pyproject.toml with [tool.deadcode]
•Best for: New projects, strict enforcement

Best practices:

•Start with high confidence thresholds (90+)
•Use whitelists for legitimate false positives
•Integrate into CI for continuous monitoring
•Combine with other tools (ruff, basedpyright, coverage)
•Review and clean regularly
•Document WHY code is whitelisted

Hybrid approach:

•Use vulture for broad detection with confidence scores
•Use deadcode for precise detection with fewer false positives
•Compare results and maintain whitelists accordingly