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