pytest vs stestr comparison of testing frameworks
What are the differences between pytest and stestr?

	pytest https://docs.pytest.org/en/latest/	stestr https://pypi.org/project/stestr/
Programming language	Python	Python
Category	Unit Testing	Unit Testing
General info	Pytest is the TDD 'all in one' testing framework for Python Pytest is a powerful Python testing framework that can test all and levels of software. It is considered by many to be the best testing framework in Python with many projects on the internet having switched to it from other frameworks, including Mozilla and Dropbox. This is due to its many powerful features such as ‘assert‘ rewriting, a third-party plugin model and a powerful yet simple fixture model.	stestr is a Python test runner designed to execute unittest test suites stestr executes unittest test suites by using multiple processes to split up execution of a test suite then stores a history of all test runs to help in debugging failures and optimizing the scheduler to improve speed.
xUnit Set of frameworks originating from SUnit (Smalltalk's testing framework). They share similar structure and functionality.	No	No
Client-side Allows testing code execution on the client, such as a web browser	Yes pytest can test any part of the stack including front-end components	Yes Stestr being a test runner that runs unittest tests, it can test fron-tend functionality and behaviour.
Server-side Allows testing the bahovior of a server-side code	Yes pytest is powerful enough to test database and server components and functionality	Yes Stestr being a test runner that runs unittest tests, it can run back-end tests for functionality and behaviour.
Fixtures Allows defining a fixed, specific states of data (fixtures) that are test-local. This ensures specific environment for a single test	Yes Pytest has a powerful yet simple fixture model that is unmatched in any other testing framework.	Yes By use of a third party library like Fixture
Group fixtures Allows defining a fixed, specific states of data for a group of tests (group-fixtures). This ensures specific environment for a given group of tests.	Yes Pytest's powerful fixture model allows grouping of fixtures	By use of a third party library like Fixture
Generators Supports data generators for tests. Data generators generate input data for test. The test is then run for each input data produced in this way.	Yes pytest has a hook function called pytest_generate_tests hook which is called when collecting a test function and one can use it to generate data	Yes By using a library like test-generator
Licence Licence type governing the use and redistribution of the software	MIT License	Apache License 2.0
Mocks Mocks are objects that simulate the behavior of real objects. Using mocks allows testing some part of the code in isolation (with other parts mocked when needed)	Yes By either using unittest.mock or using pytest-mock a thin wrapper that provides mock functionality for pytest	N/A
Grouping Allows organizing tests in groups	Yes Tests can be grouped with pytest by use of markers which are applied to various tests and one can run tests with the marker applied	N/A
Other Other useful information about the testing framework