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

stestr

https://pypi.org/project/stestr/

Green

https://github.com/CleanCut/green
Programming language

Python

Python

Category

Unit Testing

Unit Testing

General info

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.

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)
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

Stestr being a test runner that runs unittest tests, it can test fron-tend functionality and behaviour.

Yes

It can test front-end components of the django framework
Server-side
Allows testing the bahovior of a server-side code

Yes

Stestr being a test runner that runs unittest tests, it can run back-end tests for functionality and behaviour.

Yes

It can test server-side behaviours of web applications written with Python
Fixtures
Allows defining a fixed, specific states of data (fixtures) that are test-local. This ensures specific environment for a single test

Yes

By use of a third party library like Fixture

N/A

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.

By use of a third party library like Fixture

N/A

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

By using a library like test-generator

N/A

Licence
Licence type governing the use and redistribution of the software

Apache License 2.0

MIT License

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)

N/A

Yes

Through the use of Python's mock library
Grouping
Allows organizing tests in groups

N/A

N/A

Other
Other useful information about the testing framework