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

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