mocha-parallel-tests vs pytest comparison of testing frameworks
What are the differences between mocha-parallel-tests and pytest?

mocha-parallel-tests

https://www.npmjs.com/package/mocha-parallel-tests

pytest

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

JavaScript

Python

Category

Unit Testing, Intergration Testing, End-to-End Testing

Unit Testing

General info

mocha-parallel-tests is a test runner for tests written with mocha testing framework.

mocha-parallel-tests allows you to run your tests in parallel and executes each of your test files in a separate process while still maintaining the output of mocha

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.

N/A

No

Client-side
Allows testing code execution on the client, such as a web browser

Yes

Mocha-parallel-tests Runs in the browser and is used widely to test front-end components and functionality. It can test various DOM elements, front-end functions and so on.

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

Mocha-parallel-tests provides convenient ways of testing the Node server.

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

Mocha, which is the the framework which mocha-parallel-tests runs provides the hooks before(), after(), beforeEach(), and afterEach() to set up preconditions and clean up after your tests

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 available

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)

Provides Mocking capabilities through third party Libraries like sinon.js, simple-mock and nock

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

Grouping is supported and is accomplished by the using a nested 'describe()'

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