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

	TwistedTrial https://twistedmatrix.com/trac/wiki/TwistedTrial	stestr https://pypi.org/project/stestr/
Programming language	Python	Python
Category	Unit Testing, unittest Extensions	Unit Testing
General info	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'	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 Front-end components can be tested for example adding a web front-end using simple twisted.web.resource.Resource objects	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 Server-side behaviour can be tested with Trial, it has various functions for this in the twisted.web.Resource package	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 Trial supports various fixture methods such as 'setUp()' and 'tearDown' functions fixture for normal semantics of setup, and teardown	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 Methods like 'setUp()' allow for creation of group 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.	Through use of third party libraries like test-generator.	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 Trial can access the mock library inbuilt in python for mocking purposes	N/A
Grouping Allows organizing tests in groups	Yes Trial allows tests to be grouped into test packages	N/A
Other Other useful information about the testing framework