Testing#

Effective unit testing is a cornerstone of stable and reliable software. ARTKIT uses pytest, a powerful and flexible testing framework for Python. This section provides guidelines on writing unit tests with pytest to ensure your contributions align with our standards.

Getting Started with Pytest#

Pytest discovers tests in your project automatically, without the need for boilerplate code. It encourages the use of plain assert statements and is compatible with the built-in unittest framework.

If you followed the setup guide then pytest should already be installed in your development environment. The test suite can be run from the project root:

pytest

Attention

To maintain high standard for test coverage, the testing pipeline is configured to require at least 90% test coverage of the codebase, otherwise pytest will exit with a failure status.

Structure of a Test Module#

A test module is a Python file starting with test_, containing test functions that also start with test_. Here’s an example structure:

def test_example():
    assert True

ARTKIT tests are organized in the test folder at the project root. Please review the folder structure and add your tests in an appropriate sub-directory.

Guidelines for Writing Tests#

  1. Test One Thing per Function: Each test function should focus on testing a single behavior or aspect of a function. This makes tests easier to understand and debug.

  2. Use Descriptive Test Names: The function names of your tests should be descriptive enough to understand the purpose of the test. For example, test_sort_empty_list_returns_empty_list() is clear and informative.

  3. Arrange-Act-Assert Pattern: Structure your tests with setup (arrange), invocation (act), and assertion (assert) stages clearly separated. This pattern keeps tests organized and easy to follow.

  4. Leverage Fixtures for Setup and Teardown: pytest fixtures are functions run by pytest before (and sometimes after) the actual test functions. They are a powerful feature for setup and teardown operations. Use fixtures to manage resources like database connections or temporary files.

@pytest.fixture
def input_value():
    return 39

def test_divisible_by_3(input_value):
    assert input_value % 3 == 0

  1. Parametrize Tests: Use pytest.mark.parametrize to run a test function multiple times with different arguments. This is powerful for testing a function over a range of inputs.

@pytest.mark.parametrize("test_input,expected", [(3, True), (4, False)])
def test_is_odd(test_input, expected):
    assert is_odd(test_input) == expected

  1. Cover Edge Cases: Ensure your tests cover edge cases, not just the typical or expected use cases. This includes testing with empty inputs, invalid data, or extreme values.

  2. Run Tests Frequently: Run your tests frequently during development to catch regressions or unintended changes early.

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