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Testing Framework for Fin Officer

This document describes the testing framework and code quality tools set up for the Fin Officer project.

Overview

We've implemented a comprehensive testing and code quality framework that includes:

  • Unit Tests: Using pytest for testing individual components
  • Code Formatting: Using Black to ensure consistent code style
  • Linting: Using Flake8 and Pylint to catch potential issues
  • Import Sorting: Using isort to organize imports
  • Continuous Integration: Using GitHub Actions to automate testing

Running Tests

Using Tox

Tox is configured to run tests in isolated environments. To run all tests and checks:

tox

To run only the tests:

tox -e py310

To run only the linting checks:

tox -e lint

To run only the formatting checks:

tox -e format

Using Pytest Directly

To run tests directly with pytest:

python -m pytest tests/

To run a specific test file:

python -m pytest tests/test_mcp_auto_reply_unit.py

Code Quality Tools

Black

To format your code with Black:

black app/ tests/

Isort

To sort imports with isort:

isort app/ tests/

Flake8

To check your code with Flake8:

flake8 app/ tests/

Pylint

To check your code with Pylint:

pylint app/

Continuous Integration

GitHub Actions is configured to run tests and code quality checks on every push to the main branch and on pull requests. The workflow is defined in .github/workflows/python-tests.yml.

Test Coverage

To generate a test coverage report:

python -m pytest --cov=app tests/

Writing Tests

Unit Tests

Unit tests should be placed in the tests/ directory and should follow the naming convention test_*.py. Each test function should be prefixed with test_.

For asynchronous tests, use the @pytest.mark.asyncio decorator.

Example:

import pytest

@pytest.mark.asyncio
async def test_generate_auto_reply():
    # Test code here
    pass

Mocking

Use the unittest.mock module for mocking dependencies in tests.

Example:

from unittest.mock import patch, AsyncMock

@pytest.mark.asyncio
async def test_with_mocks():
    with patch('module.function', return_value=AsyncMock()):
        # Test code here
        pass

Test Data

Test data should be defined in fixtures to make it reusable across tests.

Example:

@pytest.fixture
def sample_email():
    return EmailSchema(
        id=1,
        from_email="test@example.com",
        to_email="support@finofficer.com",
        subject="Test Subject",
        content="Test Content",
        received_date="2025-05-20T00:00:00"
    )

Best Practices

  1. Write tests before implementing features (Test-Driven Development)
  2. Keep tests small and focused on a single functionality
  3. Use descriptive test names that explain what is being tested
  4. Use fixtures for common setup code
  5. Mock external dependencies to isolate the code being tested
  6. Run tests frequently during development
  7. Maintain high test coverage for critical components