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

pytest

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

Cedar

https://github.com/cedarbdd/cedar
Programming language

Python

Swift

Category

Unit Testing

Unit Testing

General info

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.

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
xUnit
Set of frameworks originating from SUnit (Smalltalk's testing framework). They share similar structure and functionality.

No

Yes

Cedar is an xUnit style framework
Client-side
Allows testing code execution on the client, such as a web browser

Yes

pytest can test any part of the stack including front-end components

Yes

You can test front-end components and behaviour with Cedar, its language is biased towards describing the behavior of your objects.
Server-side
Allows testing the bahovior of a server-side code

Yes

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

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
Fixtures
Allows defining a fixed, specific states of data (fixtures) that are test-local. This ensures specific environment for a single test

Yes

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

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
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

Pytest's powerful fixture model allows grouping of fixtures

N/A

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

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

N/A

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

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

Yes

Cedar contains inbuilt mock/test double functionality
Grouping
Allows organizing tests in groups

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

Yes

Cedar supports shared example groups. You can declare them in one of two ways: either inline with your spec declarations, or separately.
Other
Other useful information about the testing framework