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

pytest

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

Artos

https://www.theartos.com/
Programming language

Python

Java

Category

Unit Testing

Functional Testing, End-to-End 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.

Artos is an opensource BDD testing framework for writing Unit, Intergration and Functional tests

Artos includes pre-configured logging framework and extent reports, utilities to write flow for manual/semi-automated testing and supports BDD testing using cucumber scripts.
xUnit
Set of frameworks originating from SUnit (Smalltalk's testing framework). They share similar structure and functionality.

No

Yes

It is a 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

With Artos you can perform unit tests on front-end components
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 unit test server side behaviours and functionalities by testing specific back-end classes and functions
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.

N/A

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

You can use a third party library like mockito
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

Artos allows creation of test suites and they are run by use of a test script
Other
Other useful information about the testing framework