Using Pytest Markers to Categorize and Run Test Cases

Pytest markers provide a powerful way to categorize and filter test cases, improving flexibility and efficiency during test execution.

1. Setting Test Case Markers

Use the @pytest.mark decorator to attach arbitrary string tags to test functions, e.g., @pytest.mark.smoke or @pytest.mark.regression . Example:

import pytest

@pytest.mark.smoke
def test_login():
    assert True

@pytest.mark.regression
def test_update_profile():
    assert True

@pytest.mark.smoke
@pytest.mark.regression
def test_logout():
    assert True

Register custom markers in pytest.ini to make them discoverable and documented:

[pytest]
markers =
    smoke: mark test as smoke
    regression: mark test as regression
    integration: mark test as integration

2. Running Specific Marked Tests

Use the -m option to select tests by marker:

pytest -m smoke

Combine markers with logical operators:

pytest -m "smoke or regression"
pytest -m "not smoke"
pytest -m "smoke and regression"

3. Dynamically Setting Markers

Markers can be applied at runtime based on conditions:

import pytest

def test_dynamic_mark():
    if some_condition():
        pytest.mark.smoke(test_dynamic_mark)
    else:
        pytest.mark.regression(test_dynamic_mark)

4. Marker Use Cases

Smoke tests verify core functionality quickly after each build; regression tests ensure changes haven’t broken existing features; integration tests validate interactions between modules. Each type is marked accordingly and run with pytest -m commands.

5. Conclusion

By defining and registering markers, and using the -m option, developers can flexibly select and run relevant test suites, improving test coverage, efficiency, and maintainability in automated testing workflows.