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

	stestr https://pypi.org/project/stestr/	TwistedTrial https://twistedmatrix.com/trac/wiki/TwistedTrial
Programming language	Python	Python
Category	Unit Testing	Unit Testing, unittest Extensions
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.	Trial is a unit testing framework for Python built by Twisted Matrix labs Trial is composed of two parts: First is a command-line test runner, which can be run on plain Python unit tests and can do automated unit-test discovery across files, modules, or even arbitrarily nested packages. Second is a test library, derived from Python's 'unittest.TestCase'
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 Front-end components can be tested for example adding a web front-end using simple twisted.web.resource.Resource objects
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 Server-side behaviour can be tested with Trial, it has various functions for this in the twisted.web.Resource package
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	Yes Trial supports various fixture methods such as 'setUp()' and 'tearDown' functions fixture for normal semantics of setup, and teardown
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	Yes Methods like 'setUp()' allow for creation of group fixtures
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	Through use of third party libraries like test-generator.
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 Trial can access the mock library inbuilt in python for mocking purposes
Grouping Allows organizing tests in groups	N/A	Yes Trial allows tests to be grouped into test packages
Other Other useful information about the testing framework