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