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

Nose

https://nose.readthedocs.io/en/latest/

stestr

https://pypi.org/project/stestr/
Programming language

Python

Python

Category

Unit Testing, unittest Extensions

Unit Testing

General info

Nose is a Python unit test framework

This is a Python unit test framework that intergrates well with doctests, unnittests, and 'no-boilerplate tests', that is tests written from scratch without a specific boilerplate.

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

nose is a unit testing tool which is very similar to unittest. It is basically unittest with extensions therefore just like unittest is can test front-end components and behaviour

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

Nose can test back-end components and functionality as small units. One can write tests for each function that provides back-end functionality

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

nose supports fixtures at the package, module, class, and test case levels, so that initialization which can be expensive is done as infrequently as possible.

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

Group fixtures are allowed with nose, where a multitest state can be defined.

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 and from the 'unittest.TestCase' library

Yes

By using a library like test-generator
Licence
Licence type governing the use and redistribution of the software

GNU Library or Lesser General Public License (LGPL) (GNU LGPL)

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

The nose library extends the built-in Python unittest module therefore has access to unittest.mock

N/A

Grouping
Allows organizing tests in groups

Yes

With nose it collects tests automatically and there’s no need to manually collect test cases into test suites.

N/A

Other
Other useful information about the testing framework