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

Green

https://github.com/CleanCut/green

mocha-parallel-tests

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

Python

JavaScript

Category

Unit Testing

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

General info

Green is a clean, colorful, fast Python test runner

This is a test runner that has pretty printing on output that makes results easy to read and understand. Some of its features include: Tests running in independent processes (fast), low redundancy in output (clean), supports pretty printing that is the terminal output, makes good use of color when the terminal supports it (colorful)

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

No

N/A

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

Yes

It can test front-end components of the django framework

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.
Server-side
Allows testing the bahovior of a server-side code

Yes

It can test server-side behaviours of web applications written with Python

Yes

Mocha-parallel-tests provides convenient ways of testing the Node server.
Fixtures
Allows defining a fixed, specific states of data (fixtures) that are test-local. This ensures specific environment for a single test

N/A

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
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

Group fixtures are available
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

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

Through the use of Python's mock library

Provides Mocking capabilities through third party Libraries like sinon.js, simple-mock and nock
Grouping
Allows organizing tests in groups

N/A

Yes

Grouping is supported and is accomplished by the using a nested 'describe()'
Other
Other useful information about the testing framework