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

	fast-check https://github.com/dubzzz/fast-check	Selenium https://pypi.org/project/selenium/
Programming language	JavaScript	Python
Category	Unit Testing	Web Automation
General info	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	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
xUnit Set of frameworks originating from SUnit (Smalltalk's testing framework). They share similar structure and functionality.	N/A	No
Client-side Allows testing code execution on the client, such as a web browser	Yes It can test 'units' of front-end code for functionality and behaviour	Yes It is primarily a browser automation tool which tests front-end components and functionality
Server-side Allows testing the bahovior of a server-side code	Yes It is a unit testing framework in essence and can test back-end functionality and behaviour	Yes It can perform Unit tests and can test various components and behaviours in the backend using a BDD or TDD approach
Fixtures Allows defining a fixed, specific states of data (fixtures) that are test-local. This ensures specific environment for a single test	N/A	Yes By writing your Selenium WebDriver tests in PyTest, this gives you access to Pytest's powerful fixture model
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.	N/A	Yes One can group fixtures if accessing Pytest's fixture model
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.	N/A	Yes By using a library such as Faker or Fake-factory
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)	N/A	Yes It includes support for mocking
Grouping Allows organizing tests in groups	N/A	Yes By using the TestNG feature with which we can create groups and maintain them easily
Other Other useful information about the testing framework