PAL Test Generation

Create comprehensive test suites with edge case coverage.

When to Use

• Generating tests for new code
• Improving test coverage
• Identifying edge cases
• Creating regression tests
• Testing complex business logic
• Framework-specific test generation

Quick Start

result = mcp__pal__testgen(
    step="Analyzing payment processor for test coverage",
    step_number=1,
    total_steps=2,
    next_step_required=True,
    findings="Identifying critical paths, edge cases, failure modes",
    relevant_files=[
        "/app/payments/processor.py",
        "/app/payments/validation.py"
    ],
    confidence="exploring"
)

Required Parameters

| Parameter | Type | Description | |-----------|------|-------------| | step | string | Test planning narrative | | step_number | int | Current step | | total_steps | int | Estimated total | | next_step_required | bool | More planning needed? | | findings | string | Test scenarios identified |

Optional Parameters

| Parameter | Type | Description | |-----------|------|-------------| | hypothesis | string | Testing strategy | | confidence | enum | exploring → certain | | relevant_files | list | Files needing tests | | files_checked | list | All files examined | | issues_found | list | Coverage gaps found | | continuation_id | string | Continue session |

What to Document in Findings

Functionality Coverage

• Core business logic paths
• Input validation
• Error handling
• Edge cases

Test Categories

• Unit tests - Individual functions/methods
• Integration tests - Component interactions
• Edge cases - Boundary conditions
• Error cases - Failure scenarios

Example: API Endpoint Tests

mcp__pal__testgen(
    step="""
    Analyzing user registration endpoint for test coverage

    Identified paths:
    1. Happy path - valid registration
    2. Validation - email format, password strength
    3. Duplicates - existing email handling
    4. Rate limiting - abuse prevention
    5. Error cases - database failures
    """,
    step_number=1,
    total_steps=2,
    next_step_required=True,
    findings="""
    Critical test cases:
    - Valid registration creates user and returns token
    - Invalid email format returns 400
    - Weak password returns 400 with requirements
    - Duplicate email returns 409
    - Rate limit exceeded returns 429
    - Database error returns 500 and logs error

    Edge cases:
    - Unicode characters in name
    - Maximum length inputs
    - Empty string vs null vs missing field
    - SQL injection attempts (should be sanitized)
    """,
    relevant_files=[
        "/app/api/auth/register.py",
        "/app/models/user.py",
        "/tests/api/test_auth.py"
    ],
    confidence="medium"
)

Test Template Patterns

Unit Test

def test_calculate_total_with_discount():
    # Arrange
    cart = Cart(items=[Item(price=100)])
    discount = Discount(percent=10)

    # Act
    total = cart.calculate_total(discount)

    # Assert
    assert total == 90

Edge Case Test

@pytest.mark.parametrize("input,expected", [
    ("", ValidationError),
    (None, ValidationError),
    ("a" * 1000, ValidationError),
    ("valid@email.com", "valid@email.com"),
])
def test_email_validation(input, expected):
    if isinstance(expected, type) and issubclass(expected, Exception):
        with pytest.raises(expected):
            validate_email(input)
    else:
        assert validate_email(input) == expected

Best Practices

1. Test behavior, not implementation - Focus on what, not how
2. One assertion per test - Clear failure messages
3. Descriptive names - test_user_cannot_access_admin_without_role
4. Arrange-Act-Assert - Clear test structure
5. Cover edge cases - Boundaries, nulls, empty values
6. Test error paths - Failures are features too