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

Testify

https://github.com/Yelp/Testify

pytest

https://docs.pytest.org/en/latest/
Programming language

Python

Python

Category

Unit Testing

Unit Testing

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

Pytest is the TDD 'all in one' testing framework for Python

Pytest is a powerful Python testing framework that can test all and levels of software. It is considered by many to be the best testing framework in Python with many projects on the internet having switched to it from other frameworks, including Mozilla and Dropbox. This is due to its many powerful features such as ‘assert‘ rewriting, a third-party plugin model and a powerful yet simple fixture model.
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

pytest can test any part of the stack including front-end components
Server-side
Allows testing the bahovior of a server-side code

Yes

Testify can test various server and database behaviours and functionality

Yes

pytest is powerful enough to test database and server components and functionality
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

Pytest has a powerful yet simple fixture model that is unmatched in any other testing framework.
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

Pytest's powerful fixture model allows grouping of fixtures
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

pytest has a hook function called pytest_generate_tests hook which is called when collecting a test function and one can use it to generate data
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 the turtle mock object library

Yes

By either using unittest.mock or using pytest-mock a thin wrapper that provides mock functionality for pytest
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

Tests can be grouped with pytest by use of markers which are applied to various tests and one can run tests with the marker applied
Other
Other useful information about the testing framework