pytest run specific test

HaC 2025

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11-10-2024

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Running Specific Tests with pytest: A Comprehensive Guide

Pytest, a popular Python testing framework, offers powerful tools to streamline your testing workflow. One such tool is the ability to execute specific tests, crucial for focused debugging and efficient development. This article delves into the various methods of running specific tests with pytest, providing practical examples and insights to optimize your testing process.

Why Target Specific Tests?

• Debugging: When a test suite fails, pinpointing the failing test can save valuable time and effort.
• Focused Development: Running only relevant tests during development can significantly improve iteration speed.
• Feature-Specific Testing: Targeting tests related to a specific feature allows for granular analysis and ensures code changes only affect the intended areas.

Methods for Running Specific Tests

1. Using -k flag with keyword expressions:

This method allows you to specify test functions or classes based on their names or content.

Example:

pytest -k "test_addition" # runs tests containing "test_addition" in their names
pytest -k "not slow" # runs tests not containing "slow" in their names
pytest -k "class_A and method_B" # runs tests in class_A with method_B

Key points:

• -k supports regular expression syntax for more complex matching.
• Combine keywords with logical operators (and, or, not) for precise selection.

Source: pytest documentation

2. Using -m flag with markers:

Markers are annotations applied to test functions or classes, allowing for group-based test execution.

Example:

import pytest

@pytest.mark.smoke
def test_login():
    # Test login functionality
    pass

@pytest.mark.regression
def test_user_profile():
    # Test user profile updates
    pass

# Run only smoke tests
pytest -m "smoke"

# Run tests with either smoke or regression marker
pytest -m "smoke or regression"

Key points:

• Markers provide a flexible way to categorize tests for focused execution.
• Multiple markers can be applied to a single test.
• Marker names are case-sensitive.

Source: pytest documentation

3. Running individual files or directories:

Directly specify the file or directory containing your tests.

Example:

pytest test_module.py # run tests in test_module.py
pytest tests/ # run tests in the tests directory

Key points:

• This method is efficient when dealing with a small set of tests within a specific file or directory.
• Use wildcards (*) for broader selection (e.g., pytest tests/*.py to run tests in all Python files within the tests directory).

4. Utilizing pytest's -x flag to stop after the first failure:

This flag can be used in conjunction with any of the above methods to stop execution after the first test failure.

Example:

pytest -k "test_addition" -x # stop after the first test failure related to "test_addition"
pytest -m "smoke" -x # stop after the first failing smoke test

Key points:

• -x is useful for quickly identifying and addressing failures within a specific set of tests.
• Combining -x with other flags enables targeted failure detection.

5. Utilizing pytest's --last-failed flag:

This flag is useful for running only the tests that failed last time.

Example:

pytest --last-failed # run the tests that failed on the last run. 

Key points:

• --last-failed allows to run only the tests that failed previously for quick debugging.
• The results from previous runs are stored in the pytest cache.

Beyond the Basics: Advanced Usage

• Using Conftest.py: Create a conftest.py file within your test directory to define custom markers and plugins, enhancing your test workflow.
• Leveraging pytest-xdist: Run tests in parallel using pytest-xdist for faster execution, especially for large test suites.
• Exploring pytest plugins: Discover a rich ecosystem of pytest plugins that offer functionalities like test reporting, code coverage analysis, and more.

Conclusion

Pytest's ability to run specific tests empowers developers with granular control over their testing process, allowing for efficient debugging, focused development, and optimized test execution. By mastering the various methods presented here, you can streamline your testing workflow and achieve greater productivity with pytest.