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

	unexpected http://unexpected.js.org/	Green https://github.com/CleanCut/green
Programming language	JavaScript	Python
Category	Unit Testing	Unit Testing
General info	An extensible BDD assertion toolkit Unexpected is an extensible BDD assertion toolkit that is compatible with all test frameworks,is Node.js ready (require('unexpected')) and supports asynchronous assertions using promises among other features. It can be used with any test runner that catches exceptions, but the developer recommends Mocha, Jest or Jasmine as they are integrated tested with every release	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)
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 Unexpected can be used in a browser environment to test front-end components and functionality	Yes It can test front-end components of the django framework
Server-side Allows testing the bahovior of a server-side code	Yes Unexpected is used in a Node.JS environment to test server behaviour and functionality	Yes It can test server-side behaviours of web applications written with Python
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	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)	N/A	Yes Through the use of Python's mock library
Grouping Allows organizing tests in groups	N/A	N/A
Other Other useful information about the testing framework