test_template.py•5.43 kB
"""
Template for creating new test files.
Copy this file and modify it to create new tests quickly.
"""
from datetime import UTC, datetime
from unittest.mock import Mock, patch
import pytest
# Import what you're testing
# from maverick_mcp.your_module import YourClass, your_function
class TestYourClass:
"""Test suite for YourClass."""
@pytest.fixture
def mock_dependencies(self):
"""Set up common mocks for tests."""
with patch("maverick_mcp.your_module.external_dependency") as mock_dep:
mock_dep.return_value = Mock()
yield {
"dependency": mock_dep,
}
@pytest.fixture
def sample_data(self):
"""Provide sample test data."""
return {
"id": 1,
"name": "Test Item",
"value": 42.0,
"created_at": datetime.now(UTC),
}
def test_initialization(self):
"""Test class initialization."""
# obj = YourClass(param1="value1", param2=42)
# assert obj.param1 == "value1"
# assert obj.param2 == 42
pass
def test_method_success(self, mock_dependencies, sample_data):
"""Test successful method execution."""
# Arrange
# obj = YourClass()
# mock_dependencies["dependency"].some_method.return_value = "expected"
# Act
# result = obj.your_method(sample_data)
# Assert
# assert result == "expected"
# mock_dependencies["dependency"].some_method.assert_called_once_with(sample_data)
pass
def test_method_validation_error(self):
"""Test method with invalid input."""
# obj = YourClass()
# with pytest.raises(ValueError, match="Invalid input"):
# obj.your_method(None)
pass
@pytest.mark.asyncio
async def test_async_method(self, mock_dependencies):
"""Test asynchronous method."""
# Arrange
# obj = YourClass()
# mock_dependencies["dependency"].async_method = AsyncMock(return_value="async_result")
# Act
# result = await obj.async_method()
# Assert
# assert result == "async_result"
pass
class TestYourFunction:
"""Test suite for standalone functions."""
def test_function_basic(self):
"""Test basic function behavior."""
# result = your_function("input")
# assert result == "expected_output"
pass
def test_function_edge_cases(self):
"""Test edge cases."""
# Test empty input
# assert your_function("") == ""
# Test None input
# with pytest.raises(TypeError):
# your_function(None)
# Test large input
# large_input = "x" * 10000
# assert len(your_function(large_input)) <= 10000
pass
@pytest.mark.parametrize(
"input_value,expected",
[
("test1", "result1"),
("test2", "result2"),
("", ""),
("special!@#", "special"),
],
)
def test_function_parametrized(self, input_value, expected):
"""Test function with multiple inputs."""
# result = your_function(input_value)
# assert result == expected
pass
class TestIntegration:
"""Integration tests (marked for optional execution)."""
@pytest.mark.integration
def test_database_integration(self, db_session):
"""Test database operations."""
# This test requires a real database connection
# from maverick_mcp.your_module import create_item, get_item
# # Create
# item = create_item(db_session, name="Test", value=42)
# assert item.id is not None
# # Read
# retrieved = get_item(db_session, item.id)
# assert retrieved.name == "Test"
# assert retrieved.value == 42
pass
@pytest.mark.integration
@pytest.mark.asyncio
async def test_external_api_integration(self):
"""Test external API calls."""
# This test makes real API calls
# from maverick_mcp.your_module import fetch_external_data
# result = await fetch_external_data("AAPL")
# assert result is not None
# assert "price" in result
pass
# Fixtures that can be reused across tests
@pytest.fixture
def mock_redis():
"""Mock Redis client."""
with patch("maverick_mcp.data.cache.get_redis_client") as mock:
redis_mock = Mock()
redis_mock.get.return_value = None
redis_mock.set.return_value = True
mock.return_value = redis_mock
yield redis_mock
@pytest.fixture
def mock_settings():
"""Mock settings for testing."""
with patch("maverick_mcp.config.settings.settings") as mock:
mock.auth.enabled = False
mock.api.debug = True
yield mock
# Performance tests (optional)
@pytest.mark.slow
class TestPerformance:
"""Performance tests (excluded by default)."""
def test_large_dataset_processing(self):
"""Test processing of large datasets."""
# import time
# from maverick_mcp.your_module import process_data
# large_data = list(range(1_000_000))
# start = time.time()
# result = process_data(large_data)
# duration = time.time() - start
# assert len(result) == 1_000_000
# assert duration < 1.0 # Should complete in under 1 second
pass