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

	Cedar https://github.com/cedarbdd/cedar	pytest https://docs.pytest.org/en/latest/
Programming language	Swift	Python
Category	Unit Testing	Unit Testing
General info	Cedar is a BDD-style testing for swift using Objective-C Cedar is a BDD-style Objective-C/Swift testing framework that has an expressive matcher DSL and convenient test doubles (mocks). It provides better organizational facilities than the tools provided by XCTest/OCUnit In environments where C++ is available, it provides powerful built-in matchers, test doubles and fakes	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.	Yes Cedar is an xUnit style framework	No
Client-side Allows testing code execution on the client, such as a web browser	Yes You can test front-end components and behaviour with Cedar, its language is biased towards describing the behavior of your objects.	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 You can test back-end components with a bias towards their expected behaviour. Cedar specs also allow you to nest contexts so that it is easier to understand how your object behaves in different scenarios	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 Cedar has beforeEach and afterEach class methods which Cedar will look for on every class it loads. You can add these onto any class you compile into your specs and Cedar will run them	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.	N/A	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.	N/A	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	MIT License	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 Cedar contains inbuilt mock/test double functionality	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 Cedar supports shared example groups. You can declare them in one of two ways: either inline with your spec declarations, or separately.	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