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

	Green https://github.com/CleanCut/green	stestr https://pypi.org/project/stestr/
Programming language	Python	Python
Category	Unit Testing	Unit Testing
General info	Green is a clean, colorful, fast Python test runner This is a test runner that has pretty printing on output that makes results easy to read and understand. Some of its features include: Tests running in independent processes (fast), low redundancy in output (clean), supports pretty printing that is the terminal output, makes good use of color when the terminal supports it (colorful)	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 It can test front-end components of the django framework	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 It can test server-side behaviours of web applications written with Python	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	N/A	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.	N/A	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.	N/A	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 Through the use of Python's mock library	N/A
Grouping Allows organizing tests in groups	N/A	N/A
Other Other useful information about the testing framework