Green vs Crosscheck comparison of testing frameworks
What are the differences between Green and Crosscheck?

Green

https://github.com/CleanCut/green

Crosscheck

https://github.com/cross-check/cross-check
Programming language

Python

JavaScript

Category

Unit Testing

Unit 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)

Crosscheck is a JavaScript unit-testing framework capable of emulating multiple browser environments

Crosscheck is an open source testing framework for verifying your in-browser JavaScript. It helps you ensure that your code will run in many different browsers such as Internet Explorer, Chrome and Firefox, but without needing installations of those browsers. The only thing you need is a Java Virtual Machine.
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

No

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

Crosscheck is used to verify in-browser JavaScript and is a headless test framework, it tests back-end 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

N/A

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.

N/A

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.

N/A

N/A

Licence
Licence type governing the use and redistribution of the software

MIT License

N/A

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

N/A

Grouping
Allows organizing tests in groups

N/A

N/A

Other
Other useful information about the testing framework