How to Run Only Unmarked Tests In Pytest?

To run only unmarked tests in pytest, you can use the "-m" flag followed by the expression "!MARKER". This will filter out all tests with the specified marker and only run the unmarked tests. For example, to run only unmarked tests you can use the command:

pytest -m "!MARKER"

How to skip marked tests in pytest?

In pytest, you can skip tests by using the @pytest.mark.skip decorator. Here's an example of how to skip a test in pytest:

1
2
3
4
5
6

import pytest

@pytest.mark.skip(reason="Skipping this test for now")
def test_example():
    # Test code here
    assert True

In this example, the test_example test will be skipped with the given reason. When you run your tests with pytest, the skipped test will be displayed in the test report as skipped.

You can also conditionally skip tests using the @pytest.mark.skipif decorator. Here's an example of how to skip a test based on a condition:

1
2
3
4
5
6

import pytest

@pytest.mark.skipif(condition=True, reason="Condition is True, skipping test")
def test_example():
    # Test code here
    assert True

In this example, the test_example test will be skipped if the condition is True. You can adjust the condition based on your requirements to skip tests dynamically.

What is the difference between marked and unmarked tests in pytest?

In pytest, marked tests are tests that have been specifically tagged with custom markers using the @pytest.mark decorator. These markers are used to categorize and organize tests, making it easier to run specific subsets of tests or apply certain configurations to them. Marked tests can also be selected or skipped during test execution based on the markers assigned to them.

On the other hand, unmarked tests are tests that do not have any custom markers associated with them. They are just standard test functions defined within a test module or test class. Unmarked tests will be collected and run by default when running tests using pytest.

In summary, the main difference between marked and unmarked tests in pytest is that marked tests have custom markers assigned to them for categorization and selection purposes, while unmarked tests do not have any markers and are run as standard tests.

What is the purpose of running only unmarked tests in pytest?

Running only unmarked tests in pytest allows for isolating and focusing on specific tests that have not been assigned any markers. This can be useful for identifying and fixing tests that may have been overlooked or accidentally left without a marker. It can also help in ensuring that all tests are properly categorized and organized, improving the overall structure and maintainability of the test suite.

How do I exclude marked tests in pytest?

You can exclude marked tests in pytest by using the -m flag with a "not" condition. For example, if you have tests marked with @pytest.mark.slow, you can exclude those tests by running the following command:

1

pytest -m "not slow"

This will run all tests that are not marked with @pytest.mark.slow. You can replace slow with the name of any marker you want to exclude.

How to manage test cases with markers in pytest?

In pytest, you can manage test cases with markers by using the @pytest.mark decorator. Markers allow you to add metadata and labels to your test cases, which can be useful for organizing and filtering tests.

Here is an example of how you can use markers in pytest:

1. Define a marker in your test file:

1
2
3
4
5
6
7
8
9

import pytest

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

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

In this example, we have defined two test cases with markers smoke and regression.

1. Run the tests with specific markers:

You can run the tests with specific markers using the -m option in the pytest command:

1

pytest -m smoke

This will run only the test cases that are marked with the smoke marker.

1. Use markers for conditional testing:

You can also use markers for conditional testing by specifying the marker as a parameter in the test function:

1
2
3

@pytest.mark.parametrize("condition", [True, False])
def test_conditionally(condition):
    assert condition

In this example, the test_conditionally test case will be run twice, once with True and once with False as the condition parameter.

By using markers in pytest, you can easily organize and manage your test cases based on their characteristics and requirements.

What is the functionality of markers in pytest?

Markers in pytest serve as a way to add metadata or attributes to tests, allowing for more flexibility and control over the test execution process. They can be used to categorize or group tests, skip certain tests under specific conditions, run tests only in certain environments, and more.

Markers are defined using the @pytest.mark decorator and can be applied to individual test functions or classes. Some common use cases for markers in pytest include:

1. Skip: Mark tests or test classes to be skipped under specific conditions, such as when a certain dependency is missing or when running on a particular platform.
2. Run: Specify which tests to run by marking them with specific attributes, making it easier to selectively run tests based on categories or criteria.
3. Dependency: Specify dependencies between tests by marking them, allowing for better organization and control over test execution order.
4. Custom markers: Define custom markers with specific behavior or functionality tailored to your testing needs.

Overall, markers in pytest provide a powerful mechanism for organizing, managing, and controlling the execution of tests in a more flexible and customizable way.