Smart Mutation Operator Generator

Generate customized mutation operators tailored to a specific codebase to maximize mutation testing effectiveness.

Workflow

1. Analyze Codebase

Run analysis script:

# Analyze entire repository
python scripts/generate_operators.py /path/to/repo

# Analyze single file
python scripts/generate_operators.py /path/to/file.py

# Save detailed report
python scripts/generate_operators.py /path/to/repo --output operators.json

Script analyzes:

• Arithmetic operations (AOR candidates)
• Comparison operations (ROR candidates)
• Logical operations (LCR candidates)
• Constants and literals (CRP candidates)
• Function calls (FCR candidates)
• API interactions
• Database operations
• Control flow structures
• Exception handling

2. Review Generated Operators

Operator priorities:

• 🔴 High Priority: Most effective at finding bugs

  • ROR (Relational Operator Replacement)
  • LCR (Logical Connector Replacement)
  • CRP (Constant Replacement)
  • COR (Conditional Operator Replacement)

• 🟡 Medium Priority: Useful but may generate equivalent mutants

  • AOR (Arithmetic Operator Replacement)
  • FCR (Function Call Replacement)
  • RVR (Return Value Replacement)
  • API/DB operation mutations

• 🔵 Low Priority: Often trivial or equivalent

  • UOI/UOD (Unary Operator Insertion/Deletion)
  • STR (String Replacement)
  • IHI/IHD (Hiding Variable Insertion/Deletion)

3. Customize for Project

Consider project characteristics:

Financial/calculation-heavy code:

• Prioritize AOR (arithmetic operators)
• Focus on CRP (numeric constants)
• Test boundary conditions

API-heavy code:

• Custom API call mutations
• Timeout/retry logic mutations
• Error response mutations

Business logic code:

• ROR (comparisons in business rules)
• LCR (logical conditions)
• COR (conditional branches)

Data processing code:

• Collection mutations
• Null/empty value mutations
• Type conversion mutations

4. Define Custom Operators

Example custom operators:

API Timeout Mutation:

# Original
response = api.call(timeout=30)

# Mutant
response = api.call(timeout=1)  # Test timeout handling

Retry Logic Mutation:

# Original
@retry(max_attempts=3)
def fetch_data():
    pass

# Mutant
@retry(max_attempts=1)  # Test with fewer retries
def fetch_data():
    pass

Business Rule Mutation:

# Original
if age >= 18 and has_license:
    allow_driving()

# Mutants
if age > 18 and has_license:   # ROR
if age >= 18 or has_license:   # LCR
if age >= 21 and has_license:  # CRP (domain-specific)

5. Avoid Trivial Mutations

Skip equivalent mutants:

# Equivalent (don't mutate)
x = a + 0  # → x = a - 0 (still equals a)
x = a * 1  # → x = a / 1 (still equals a)

# Non-equivalent (do mutate)
x = a + 1  # → x = a - 1 (different result)
x = a * 2  # → x = a / 2 (different result)

Skip trivial mutations:

# Trivial (low value)
message = "Hello"  # → message = "" (obvious failure)

# Valuable (high value)
if count > 0:      # → if count >= 0 (subtle boundary)

6. Generate Mutation Test Plan

Document operator selection:

MUTATION TESTING PLAN
=====================

Project: MyApp
Language: Python
Files Analyzed: 45

SELECTED OPERATORS
------------------

High Priority (Apply to all code):
1. ROR - Relational Operator Replacement
   - Target: Comparison operations in business logic
   - Expected: 150 mutants
   - Rationale: Critical for boundary condition testing

2. LCR - Logical Connector Replacement
   - Target: Boolean expressions in validation
   - Expected: 80 mutants
   - Rationale: Tests logical condition coverage

3. CRP - Constant Replacement
   - Target: Numeric thresholds and limits
   - Expected: 120 mutants
   - Rationale: Tests boundary values

Medium Priority (Apply selectively):
4. AOR - Arithmetic Operator Replacement
   - Target: Calculation functions only
   - Expected: 60 mutants
   - Rationale: Financial calculations need thorough testing

Custom Operators:
5. RETRY - Retry Logic Mutation
   - Target: @retry decorators
   - Expected: 15 mutants
   - Rationale: Test resilience mechanisms

ESTIMATED RESULTS
-----------------
Total Mutants: ~425
Mutation Score Target: >80%
Execution Time: ~25 minutes

7. Implement with Mutation Tool

Python (mutmut):

mutmut run --paths-to-mutate=src/
mutmut results

JavaScript (Stryker):

// stryker.conf.js
module.exports = {
  mutator: {
    excludedMutations: ['StringLiteral', 'UnaryOperator']
  }
};

Quick Reference

Standard Operators

See standard_operators.md for complete list.

Most effective:

• ROR:

  >

  →

  >=

  ,

  <

  ,

  ==

• LCR:

  and

  →

  or

• CRP:

  0

  →

  1

  ,

  true

  →

  false

• COR:

  if x:

  →

  if not x:

Mutation Score

Mutation Score = (Killed Mutants / Total Mutants) × 100%

Targets:

• Excellent: >80%
• Good: 60-80%
• Needs improvement: <60%

Best Practices

Start small:

• Begin with high-priority operators
• Test on small modules first
• Gradually expand coverage

Focus on critical code:

• Business logic
• Security-sensitive code
• Complex algorithms
• Error handling

Avoid over-mutation:

• Skip trivial mutations
• Exclude equivalent mutants
• Focus on meaningful changes

Review results:

• Killed: Tests are effective
• Survived: Add tests or improve existing ones
• Equivalent: Mark and exclude

Helper Script

# Analyze repository
python scripts/generate_operators.py /path/to/repo

# Save detailed report
python scripts/generate_operators.py /path/to/repo --output report.json

Output includes:

• Detected code patterns
• Recommended operators by priority
• Estimated mutant count
• Customization suggestions