mutation-testing

STARTER_CHARACTER = 🧬🔬

Mutation Testing

Mutation testing answers the question: "Are my tests actually catching bugs?"

Code coverage tells you what code your tests execute. Mutation testing tells you if your tests would detect changes to that code. A test suite with 100% coverage can still miss 40% of potential bugs.

Core Concept

The Mutation Testing Process:

1. Generate mutants: Introduce small bugs (mutations) into production code
2. Run tests: Execute your test suite against each mutant
3. Evaluate results: If tests fail, the mutant is "killed" (good). If tests pass, the mutant "survived" (bad - your tests missed the bug)

The Insight: A surviving mutant represents a bug your tests wouldn't catch.

When to Use

Use mutation testing analysis when:

• Reviewing code changes on a branch
• Verifying test effectiveness after TDD
• Identifying weak tests that appear to have coverage
• Finding missing edge case tests
• Validating that refactoring didn't weaken test suite

Integration with TDD:

TDD Workflow                    Mutation Testing Validation
┌─────────────────┐             ┌─────────────────────────────┐
│ RED: Write test │             │                             │
│ GREEN: Pass it  │──────────►  │ After GREEN: Verify tests   │
│ REFACTOR        │             │ would kill relevant mutants │
└─────────────────┘             └─────────────────────────────┘

Systematic Branch Analysis Process

Follow this systematic process when analyzing code on a branch:

Step 1: Identify Changed Code

# For JavaScript/TypeScript
git diff main...HEAD --name-only | grep -E '\.(ts|js|tsx|jsx)$' | grep -v '\.test\.'

# For Python
git diff main...HEAD --name-only | grep '\.py$' | grep -v 'test_'

# Get detailed diff for analysis
git diff main...HEAD -- src/

Step 2: Generate Mental Mutants

For each changed function/method, mentally apply mutation operators (see Language-Specific Operators below).