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

	Python Testify https://github.com/Yelp/Testify	Selenium https://pypi.org/project/selenium/
Programming language	Python	Python
Category	Unit Testing	Web Automation
General info	A Python unit testing framework modelled after unittest Testify is modelled after unittest but has more features while still supporting unittest classes. It has more pythonic naming conventions, an better test runner output visually, a decorator-based approach to fixture methods among many other features	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.	No	No
Client-side Allows testing code execution on the client, such as a web browser	Yes Front-end functionality and behaviour can be tested by Testify.	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 Testify can test various server and database behaviours and functionality	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	Yes Fixture methods are supported and it follows a decorator based approach, that is they are written similar to decorators	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.	Yes Group fixtures are supported	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.	Yes One can create generator methods to yield runnable test methods which will pick out the test methods from your TestCases, and then exclude any in any of your exclude_suites method.If there are any require_suites, it will then further limit itself to test methods in those suites.	Yes By using a library such as Faker or Fake-factory
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 the turtle mock object library	Yes It includes support for mocking
Grouping Allows organizing tests in groups	Yes Testify includes support for detecting and running test suites, grouped by modules, classes, or individual test methods.	Yes By using the TestNG feature with which we can create groups and maintain them easily
Other Other useful information about the testing framework