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

	TwistedTrial https://twistedmatrix.com/trac/wiki/TwistedTrial	pytest https://docs.pytest.org/en/latest/
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'	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.
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 pytest can test any part of the stack including front-end components
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 pytest is powerful enough to test database and server components and functionality
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 Pytest has a powerful yet simple fixture model that is unmatched in any other testing framework.
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	Yes Pytest's powerful fixture model allows grouping of 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.	Through use of third party libraries like test-generator.	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
Licence Licence type governing the use and redistribution of the software	MIT License	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)	Yes Trial can access the mock library inbuilt in python for mocking purposes	Yes By either using unittest.mock or using pytest-mock a thin wrapper that provides mock functionality for pytest
Grouping Allows organizing tests in groups	Yes Trial allows tests to be grouped into test packages	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
Other Other useful information about the testing framework