Testing Expertise

Language-agnostic testing methodologies, strategies, and best practices for building reliable, maintainable test suites.

Testing Methodologies

Test-Driven Development (TDD)

Red-Green-Refactor Cycle:

1. Red: Write a failing test that describes desired behavior
2. Green: Write minimal code to make the test pass
3. Refactor: Improve code while keeping tests passing
4. Repeat: Continue with next behavior

Behavior-Driven Development (BDD)

Given-When-Then Pattern:

• Given: Setup - establish the initial context
• When: Action - perform the behavior being tested
• Then: Assertion - verify the expected outcome

Test Strategy Development

What to Test

• Core functionality: Primary features and business logic
• Edge cases: Boundary conditions and unusual inputs
• Error paths: Failure scenarios and error handling
• Boundary conditions: Min/max values, empty inputs, null handling

What Not to Test

• Private implementation details: Test public interfaces
• External dependencies: Mock or stub external services
• Framework code: Trust well-tested frameworks
• Trivial code: Getters/setters without logic

Coverage Strategies

• Meaningful coverage: Test behavior, not just lines of code
• Risk-based testing: More tests for high-risk, critical functionality
• Diminishing returns: 100% coverage is not always the goal
• Coverage metrics: Use as guidance, not targets

Test Organization

Test Structure Principles

AAA Pattern (Arrange-Act-Assert):

test "feature works correctly" do
  # Arrange - Set up test data and preconditions
  user = create_test_user()

  # Act - Execute the behavior being tested
  result = perform_action(user)

  # Assert - Verify expected outcomes
  assert result == expected_value
end

Test Naming

Clear, descriptive test names that explain:

• What is being tested
• Under what conditions
• What the expected outcome is

Examples:

• test_user_creation_with_valid_data_succeeds
• test_authentication_with_invalid_password_fails
• test_empty_cart_checkout_returns_error

File Organization

• Mirror source structure: Tests should parallel production code structure
• Separate by type: Unit, integration, and E2E tests in different directories
• Group related tests: Use describe/context blocks for related scenarios

Test Quality Principles

Test Isolation and Independence

• Each test should run in isolation
• Tests should not depend on other tests
• Tests should not share mutable state
• Order of test execution should not matter

Test Readability

• One assertion per test when practical
• Clear test names that describe behavior
• Minimal test setup (use helpers/fixtures)
• Self-documenting test code

Test Maintainability

• DRY principle in tests: Extract common setup to helpers
• But prefer clarity: Some duplication is okay for readability
• Update tests with code: Keep tests in sync with production code
• Remove obsolete tests: Delete tests for removed features

Fast Test Execution

• Unit tests should be very fast (< 100ms each)
• Integration tests should be reasonably fast (< 1s when possible)
• Use test parallelization when available
• Mock expensive operations (database, network, file I/O)

Mock/Stub Strategies

When to Mock

• External services (APIs, databases, file systems)
• Slow operations that would make tests slow
• Non-deterministic behavior (random, time, network)
• Hard-to-reproduce scenarios (errors, edge cases)

When Not to Mock

• Simple logic that's fast to execute
• Code you own and control
• When mocking makes test more complex than the code
• When you're testing integration between components

Avoiding Over-Mocking

• Mock at boundaries, not internal details
• Test real integrations when feasible
• Use real objects for simple dependencies
• Verify mocks don't drift from real implementations

Common Test Smells

Test Interdependence

Problem: Tests that depend on execution order or shared state Solution: Ensure complete test isolation with proper setup/teardown

Obscure Tests

Problem: Tests that are hard to understand Solution: Clear names, good structure, minimal setup

Slow Tests

Problem: Tests that take too long to run Solution: Mock expensive operations, optimize test data

Fragile Tests

Problem: Tests that break on unrelated changes Solution: Test behavior not implementation, avoid over-specification

Flaky Tests

Problem: Tests that sometimes pass and sometimes fail Solution: Avoid timing dependencies, ensure deterministic setup

Test Data Management

Fixtures

• Pre-defined test data loaded from files
• Good for consistent, reusable test scenarios
• Can become stale and hard to maintain

Factories/Builders

• Programmatically generate test data
• Flexible and maintainable
• Can specify only relevant attributes
• Preferred for most cases

Inline Data

• Data defined directly in test
• Good for simple, one-off scenarios
• Makes test self-contained

Testing Best Practices

General Principles

1. Keep tests simple: Tests should be easier to understand than production code
2. Test one thing: Each test should verify one specific behavior
3. Make failures obvious: Clear assertions with good error messages
4. Maintain tests: Refactor tests as you refactor code
5. Run tests frequently: Fast feedback loop is essential

Error Messages

• Use descriptive assertion messages
• Include actual and expected values
• Provide context about what was being tested
• Help diagnose failures quickly

Test Performance

• Fast unit tests: Run entire suite in seconds
• Optimize setup: Share expensive setup when safe
• Parallel execution: Run independent tests concurrently
• Skip slow tests: Mark and run separately in CI

Coverage Guidelines

Meaningful Coverage Metrics

• Line coverage: Percentage of code lines executed
• Branch coverage: Percentage of code branches taken
• Function coverage: Percentage of functions called
• Useful metric: Tests actually exercise different scenarios

Coverage Goals

• Aim for high coverage on critical paths
• Accept lower coverage on trivial code
• Use coverage to find untested areas
• Don't game the metrics with meaningless tests

Test Types

Unit Tests

• Test individual functions/methods in isolation
• Fast, focused, numerous
• Mock dependencies
• Verify logic correctness

Integration Tests

• Test component interactions
• May use real dependencies (database, etc.)
• Verify components work together
• Slower than unit tests

End-to-End Tests

• Test complete user flows
• Use real system (or close simulation)
• Verify system behavior from user perspective
• Slowest, most comprehensive

Property-Based Testing

• Generate random inputs within constraints
• Verify invariants hold across inputs
• Find edge cases you didn't think of
• Complement example-based tests

Testing Anti-Patterns

• Testing implementation details: Test behavior, not internals
• One giant test: Break into focused, smaller tests
• Testing everything: Focus on valuable tests
• Ignoring test failures: Fix or remove failing tests
• Copying production bugs: Tests should prevent bugs, not preserve them