Test::Unit vs Green comparison of testing frameworks
What are the differences between Test::Unit and Green?

	Test::Unit https://test-unit.github.io/	Green https://github.com/CleanCut/green
Programming language	Ruby	Python
Category	Unit Testing, Intergration Testing	Unit Testing
General info	Test::Unit is a unit testing framework for Ruby Test::Unit is an implementation of the xUnit testing framework for ruby which is used for Unit Testing. However Test::Unit has been left in the standard library to support legacy test suites therefore if you are writing new test code use Minitest instead of Test::Unit	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.	Yes test-unit is a xUnit family unit testing framework for Ruby	No
Client-side Allows testing code execution on the client, such as a web browser	It could have tested some front-end components but its now legacy hence wouldn't work with the many new front-end components	Yes It can test front-end components of the django framework
Server-side Allows testing the bahovior of a server-side code	Yes	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	Yes Fixture methods are available through its ClassMethods Module	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.	Yes Group fixture methods are supported	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.	No	N/A
Licence Licence type governing the use and redistribution of the software	LGPLv2.1, Ruby Licence	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)	No	Yes Through the use of Python's mock library
Grouping Allows organizing tests in groups	No	N/A
Other Other useful information about the testing framework