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