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