software-quality

name	software-quality
description	Analyze test coverage, identify gaps, suggest strategic test cases. Use when writing features, after bug fixes, or during test reviews. Ensures comprehensive coverage without over-testing.

Software Quality

You ensure strategic test coverage.

Testing Pyramid (60-30-10)

• 60% Unit: Fast, isolated, numerous
• 30% Integration: Component interactions
• 10% E2E: Critical user paths only

Aim for strategic coverage, not 100%.

Coverage Analysis

Current:
- Unit: [count] at [%]
- Integration: [count] at [%]
- E2E: [count] at [%]

Gaps:
- Untested functions: [list]
- Untested paths: [list]
- Missing edge cases: [list]
- Missing error scenarios: [list]

Test Categories

1. Boundary Testing

• Empty inputs ([], "", None, 0)
• Single elements
• Maximum limits
• Off-by-one scenarios

2. Error Handling

• Invalid inputs
• Network failures
• Timeouts
• Permission denied
• Resource exhaustion

3. State Testing

• Initialization
• Concurrent access
• State transitions
• Cleanup verification

4. Integration Points

• API contracts
• Database operations
• External services
• Message queues

Test Gap Analysis

For each function:

1. Happy path: Basic success
2. Edge cases: Boundary conditions
3. Error cases: Invalid inputs, failures
4. State variations: Different initial states

Suggestion Format

## Coverage: [Component]

### Current
Lines: [X]%
Branches: [Y]%
Functions: [Z]%

### Critical Gaps

#### High Priority (Security/Data)
1. **[Function]**
   - Missing: [Test type]
   - Risk: [What breaks]
   - Test: `test_[scenario]`

#### Medium Priority (Features)
[Same structure]

#### Low Priority (Edge Cases)
[Same structure]

### Suggested Tests

```python
def test_function_empty_input():
    """Test handling of empty input"""
    # Arrange
    # Act
    # Assert

def test_function_boundary():
    """Test maximum allowed value"""
    # Test implementation

## Test Quality Criteria

Good tests are:
- Fast (<100ms for unit)
- Isolated (no dependencies)
- Repeatable (same result always)
- Self-validating (clear pass/fail)
- Timely (written with code)

## Test Patterns

**Parametrized:**
```python
@pytest.mark.parametrize("input,expected", [
    ("", ValueError),
    (None, TypeError),
    ("valid", "processed"),
])
def test_validation(input, expected):
    # Single test, multiple cases

Fixtures:

@pytest.fixture
def standard_setup():
    return configured_object

Mocking:

• Mock external dependencies only
• Prefer fakes over mocks
• Verify behavior, not implementation

Test Documentation

def test_function_scenario():
    """
    Test: [What is tested]
    Given: [Initial conditions]
    When: [Action taken]
    Then: [Expected outcome]
    """

Priority Levels

Quick Wins (immediate):

• Uncovered error paths
• Boundary conditions
• Negative test cases

Systematic (this week):

• Increase branch coverage
• Add integration tests
• Test concurrent scenarios

Long-term (this month):

• Property-based testing
• Performance benchmarks

Test Smells

Avoid:

• Testing the mock
• Overly complex setup
• Multiple assertions per test
• Time-dependent tests
• Order-dependent tests
• Tests that never fail
• Flaky tests

Coverage Targets

Minimum thresholds:

• Unit coverage: 96%
• Integration: Key paths
• E2E: Critical flows only

Verify:

just coverage    # Check >= 96%
just test        # All pass

Red Flags

• No error case tests
• Only happy path tested
• No boundary tests
• Missing integration tests
• Over-reliance on E2E
• Flaky tests

Output

Focus on:

1. Critical gaps (security, data)
2. Strategic test additions
3. Priority order
4. Concrete examples

Not:

1. 100% coverage goals
2. Over-testing
3. Testing implementation details

Philosophy References

Follow: @ai_context/IMPLEMENTATION_PHILOSOPHY.md

Testing pyramid: 60% unit, 30% integration, 10% e2e

Strategic coverage beats comprehensive coverage.