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

Selenium

https://pypi.org/project/selenium/

stestr

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

Python

Python

Category

Web Automation

Unit Testing

General info

Selenium is an open source tool used to test web applications

Selenium is a powerful testing tool which can send standard Python commands to different browsers, despite variations in browser design. It also provides extensions to emulate user interaction with browsers, a distribution server for scaling browser allocation, and the infrastructure for implementations of the W3C WebDriver specification that lets you write interchangeable code for all major web browsers

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

It is primarily a browser automation tool which tests front-end components and functionality

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

It can perform Unit tests and can test various components and behaviours in the backend using a BDD or TDD approach

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

By writing your Selenium WebDriver tests in PyTest, this gives you access to Pytest's powerful fixture model

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

One can group fixtures if accessing Pytest's fixture model

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.

Yes

By using a library such as Faker or Fake-factory

Yes

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

Apache License 2.0

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

It includes support for mocking

N/A

Grouping
Allows organizing tests in groups

Yes

By using the TestNG feature with which we can create groups and maintain them easily

N/A

Other
Other useful information about the testing framework