---
name: testing-principles
description: Language-agnostic testing principles including TDD, test quality, coverage standards, and test design patterns. Use when writing tests, designing test strategies, or reviewing test quality.
---

# Language-Agnostic Testing Principles

## Core Testing Philosophy

1. **Tests are First-Class Code**: Maintain test quality equal to production code
2. **Fast Feedback**: Tests should run quickly and provide immediate feedback
3. **Reliability**: Tests should be deterministic and reproducible
4. **Independence**: Each test should run in isolation

## Test-Driven Development (TDD)

### The RED-GREEN-REFACTOR Cycle

**Always follow this cycle:**

1. **RED**: Write a failing test first
   - Write the test before implementation
   - Ensure the test fails for the right reason
   - Verify test can actually fail

2. **GREEN**: Write minimal code to pass
   - Implement just enough to make the test pass
   - Focus on making it work

3. **REFACTOR**: Improve code structure
   - Clean up implementation
   - Eliminate duplication
   - Improve naming and clarity
   - Keep all tests passing

4. **VERIFY**: Ensure all tests still pass
   - Run full test suite
   - Check for regressions
   - Validate refactoring didn't break anything

### TDD Benefits

- Better design through testability requirements
- Comprehensive test coverage by default
- Living documentation of expected behavior
- Confidence to refactor

## Quality Requirements

### Coverage Standards

- **Minimum 80% code coverage** for production code
- Prioritize critical paths and business logic
- Prioritize meaningful assertions over coverage percentage
- Use coverage as a guide, not a goal

### Test Characteristics

All tests must be:

- **Independent**: No dependencies between tests (see Test Independence Verification for detailed criteria)
- **Reproducible**: Same input always produces same output
- **Fast**: Unit tests < 100ms each, integration tests < 1s each, full suite < 10 minutes
- **Self-checking**: Clear pass/fail without manual verification
- **Timely**: Written close to the code they test

## Test Types

### Unit Tests

**Purpose**: Test individual components in isolation

**Characteristics**:
- Test single function, method, or class
- Fast execution (milliseconds)
- No external dependencies
- Mock external services
- Majority of your test suite

**Example Scope**:
```
✓ Test calculateTotal() function
✓ Test UserValidator class
✓ Test parseDate() utility
```

### Integration Tests

**Purpose**: Test interactions between components

**Characteristics**:
- Test multiple components together
- May include database, file system, or APIs
- Slower than unit tests
- Verify contracts between modules
- Smaller portion of test suite

**Example Scope**:
```
✓ Test UserService with Database
✓ Test API endpoint with authentication
✓ Test file processing pipeline
```

### End-to-End (E2E) Tests

**Purpose**: Test complete workflows from user perspective

**Characteristics**:
- Test entire application stack
- Simulate real user interactions
- Slowest test type
- Fewest in number
- Highest confidence level

**Example Scope**:
```
✓ Test user registration flow
✓ Test checkout process
✓ Test complete report generation
```

## Test Design Principles

### AAA Pattern (Arrange-Act-Assert)

Structure every test in three clear phases:

```
// Arrange: Setup test data and conditions
user = createTestUser()
validator = createValidator()

// Act: Execute the code under test
result = validator.validate(user)

// Assert: Verify expected outcome
assert(result.isValid == true)
```

**Adaptation**: Apply this structure using your language's idioms (methods, functions, procedures)

### One Assertion Per Concept

- Test one behavior per test case
- Multiple assertions OK if testing single concept
- Split unrelated assertions into separate tests

**Good**:
```
test("validates user email format")           // input validation
test("returns error when service unavailable") // error path
test("transitions order from pending to paid") // state transition
```

**Bad**:
```
test("validates user") // Tests everything at once
```

### Descriptive Test Names

Test names should clearly describe:
- What is being tested
- Under what conditions
- What the expected outcome is

**Recommended format**: `"should [expected behavior] when [condition]"`

**Examples**:
```
test("should return error when email is invalid")
test("should calculate discount when user is premium")
test("should throw exception when file not found")
```

**Adaptation**: Follow your project's naming convention (camelCase, snake_case, describe/it blocks)

## Test Independence

### Setup and Teardown

- Use setup hooks to prepare test environment
- Use teardown hooks to clean up resources
- Keep setup minimal and focused
- Ensure teardown runs even if test fails

## Mocking and Test Doubles

### When to Use Mocks

- **Mock external dependencies**: APIs, databases, file systems
- **Mock slow operations**: Network calls, heavy computations
- **Mock unpredictable behavior**: Random values, current time
- **Mock unavailable services**: Third-party services

### Mocking Principles

- Mock at boundaries, not internally
- Keep mocks simple and focused
- Verify mock expectations when relevant
- Wrap external libraries/frameworks behind adapters and mock the adapter

## Data Layer Testing

### Mock Limitations for Data Layer

Mocks validate call patterns but cannot verify data layer correctness. The following pass through undetected with mock-only testing:
- Schema mismatches (table names, column names, data types)
- Query correctness (joins, filters, aggregations, grouping)
- Database constraints (NOT NULL, UNIQUE, foreign keys)
- Migration drift (schema changes that make code out of sync)

### When Mocks Are Appropriate for Data Access

- Testing business logic that receives data from the data layer (mock the repository, test the service)
- Testing error handling paths (simulating connection failures, timeouts)
- Unit tests where data access is a dependency, not the subject under test

### When Mocks Are Insufficient for Data Access

- Testing repository or data access implementations themselves
- Verifying query correctness (joins, filters, aggregations, grouping)
- Testing data integrity constraints
- Testing migration compatibility

### Real Database Testing (Environment-Dependent)

Options for verifying data layer correctness against a real database engine:
- **Containerized databases** for CI environments
- **In-memory databases** for fast feedback (note: dialect differences may mask issues)
- **Dedicated test databases** with seed data

The appropriate approach depends on project environment and CI/CD capabilities.

### AI-Generated Code and Schema Awareness

- AI-generated data access code has heightened schema hallucination risk
- Generated queries may use correct syntax but reference nonexistent schema elements
- Mock-based tests pass regardless of schema accuracy
- Mitigation: Design Docs should include explicit schema references so that documented schemas can be cross-checked against data access code during review

## Test Quality Practices

### Keep Tests Active

- **Fix or delete failing tests**: Resolve failures immediately
- **Remove commented-out tests**: Fix them or delete entirely
- **Keep tests running**: Broken tests lose value quickly
- **Maintain test suite**: Refactor tests as needed

### Test Helpers and Utilities

- Create reusable test data builders
- Extract common setup into helper functions
- Build test utilities for complex scenarios
- Share helpers across test files appropriately

## What to Test

### Focus on Behavior

**Test observable behavior, not implementation**:

✓ **Good**: Test that function returns expected output
✓ **Good**: Test that correct API endpoint is called
✗ **Bad**: Test that internal variable was set
✗ **Bad**: Test order of private method calls

### Test Public APIs

- Test through public interfaces
- Avoid testing private methods directly
- Test return values, outputs, exceptions
- Test side effects (database, files, logs)

### Test Edge Cases

Always test:
- **Boundary conditions**: Min/max values, empty collections
- **Error cases**: Invalid input, null values, missing data
- **Edge cases**: Special characters, extreme values
- **Happy path**: Normal, expected usage

## Test Quality Criteria

These criteria ensure reliable, maintainable tests.

### Literal Expected Values

- Use hardcoded literal values in assertions
- Calculate expected values independently from the implementation
- If the implementation has a bug, the test catches it through independent verification
- If expected value equals mock return value unchanged, the test verifies nothing (no transformation occurred)

### Result-Based Verification

- Verify final results and observable outcomes
- Assert on return values, output data, or system state changes
- For mock verification, check that correct arguments were passed

### Meaningful Assertions

- Every test must include at least one assertion
- Assertions must validate observable behavior
- A test without assertions always passes and provides no value

### Appropriate Mock Scope

- Mock direct external I/O dependencies: databases, HTTP clients, file systems
- Use real implementations for internal utilities and business logic
- Over-mocking reduces test value by verifying wiring instead of behavior

### Boundary Value Testing

Test at boundaries of valid input ranges:
- Minimum valid value
- Maximum valid value
- Just below minimum (invalid)
- Just above maximum (invalid)
- Empty input (where applicable)

### Test Independence Verification

Each test must:
- Create its own test data
- Not depend on execution order
- Clean up its own state
- Pass when run in isolation

## Verification Requirements

### Before Commit

- ✓ All tests pass — fix failing tests immediately
- ✓ No tests skipped or commented — delete or fix
- ✓ No debug code left in tests
- ✓ Test coverage meets standards
- ✓ No flaky tests — make deterministic
- ✓ Tests run within performance thresholds

## Test Organization

### File Structure

- **Mirror production structure**: Tests follow code organization
- **Clear naming conventions**: Follow project's test file patterns
  - Examples: `UserService.test.*`, `user_service_test.*`, `test_user_service.*`, `UserServiceTests.*`
- **Logical grouping**: Group related tests together
- **Separate test types**: Unit, integration, e2e in separate directories

### Test Suite Organization

```
tests/
├── unit/           # Fast, isolated unit tests
├── integration/    # Integration tests
├── e2e/            # End-to-end tests
├── fixtures/       # Test data and fixtures
└── helpers/        # Shared test utilities
```

## Performance Considerations

### Test Speed

- **Unit tests**: < 100ms each
- **Integration tests**: < 1s each
- **Full suite**: Should run frequently (< 10 minutes)

### Optimization Strategies

- Run tests in parallel when possible
- Use in-memory databases for tests
- Mock expensive operations
- Split slow test suites
- Profile and optimize slow tests

## Continuous Integration

### CI/CD Requirements

- Run full test suite on every commit
- Block merges if tests fail
- Run tests in isolated environments
- Test on target platforms/versions

### Test Reports

- Generate coverage reports
- Track test execution time
- Identify flaky tests
- Monitor test trends

## Test Design Guardrails

### Every Test Must

- Include at least one meaningful assertion
- Create its own test data and clean up its own state
- Pass when run in any order and in isolation
- Test observable behavior through public interfaces
- Keep test logic simple (no branching, no loops)
- Mock only external I/O boundaries, use real implementations for internal logic

### Flaky Test Resolution

- Use deterministic time mocking instead of real clocks
- Use fixed seed values instead of random data
- Ensure proper resource cleanup in teardown
- Resolve race conditions with synchronization primitives

## Regression Testing

### Prevent Regressions

- Add test for every bug fix
- Maintain comprehensive test suite
- Run full suite regularly
- Keep all tests unless the tested functionality is removed

### Legacy Code

- Add characterization tests before refactoring
- Test existing behavior first
- Gradually improve coverage
- Refactor with confidence

## Testing Best Practices by Language Paradigm

### Type System Utilization

**For languages with static type systems:**
- Leverage compile-time verification for correctness
- Focus tests on business logic and runtime behavior
- Use language's type system to prevent invalid states

**For languages with dynamic typing:**
- Add comprehensive runtime validation tests
- Explicitly test data contract validation
- Consider property-based testing for broader coverage

### Programming Paradigm Considerations

**Functional approach:**
- Test pure functions thoroughly (deterministic, no side effects)
- Test side effects at system boundaries
- Leverage property-based testing for invariants

**Object-oriented approach:**
- Test behavior through public interfaces
- Mock dependencies via abstraction layers
- Test polymorphic behavior carefully

**Common principle:** Adapt testing strategy to leverage language strengths while ensuring comprehensive coverage

## Documentation and Communication

### Tests as Documentation

- Tests document expected behavior
- Use clear, descriptive test names
- Include examples of usage
- Show edge cases and error handling

### Test Failure Messages

- Provide clear, actionable error messages
- Include actual vs expected values
- Add context about what was being tested
- Make debugging easier