Selenium vs fast-check comparison of testing frameworks
What are the differences between Selenium and fast-check?

Selenium

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

fast-check

https://github.com/dubzzz/fast-check
Programming language

Python

JavaScript

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

It's a property based testing framework written in typescript

Fast-check provides another way to test programs by using property testing, Property testing is a way to test functionality by automatically generating many different inputs. This means Instead of relying on hard-coded inputs and outputs, it checks characteristics of the output given the whole range of possible inputs
xUnit
Set of frameworks originating from SUnit (Smalltalk's testing framework). They share similar structure and functionality.

No

N/A

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

It can test 'units' of front-end code for 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

It is a unit testing framework in essence and can test back-end 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

N/A

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

N/A

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

N/A

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)

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