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

	Green https://github.com/CleanCut/green	Unitils http://www.unitils.org/summary.html
Programming language	Python	Java
Category	Unit Testing	Unit Testing, Intergration 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)	Unitils is an open source library whose goal is to make unit and integration testing easy and maintainable. Unitils is divided into modules, each of them providing support for a certain aspect of your unit and integration tests. For example if you need mocking for your tests, just include unitils-mock as a dependency, If you would also want to load DbUnit data sets, just include unitils-dbunit
xUnit Set of frameworks originating from SUnit (Smalltalk's testing framework). They share similar structure and functionality.	No	No
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 You can perform unit tests on various components and functionality that make up the front-end
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 Unitils provides support for testing the back-end through modules such as DbUnit which specifically tests your database, i.e connections setup and so on
Fixtures Allows defining a fixed, specific states of data (fixtures) that are test-local. This ensures specific environment for a single test	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
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
Licence Licence type governing the use and redistribution of the software	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
Grouping Allows organizing tests in groups	N/A
Other Other useful information about the testing framework