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

	HavaRunner https://github.com/havarunner/havarunner	Green https://github.com/CleanCut/green
Programming language	Java	Python
Category	Unit Testing	Unit Testing
General info	HavaRuner is a Java test framework with built-in concurrency support, suites and scenarios HavaRunner is a Java test framework that has built in support for concurrency and enables you to create suites. You can run the same test against multiple scenarios and speeds up development cycles with faster tests.HavaRunner is a JUnit runner, which means that it is built on top of JUnit it's fairly straightforward to adopt it in a codebase that already has JUnit tests.	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.	No	No
Client-side Allows testing code execution on the client, such as a web browser	Yes You can test front-end functionality and components with havarunner	Yes It can test front-end components of the django framework
Server-side Allows testing the bahovior of a server-side code	Yes HavaRunner is able to test server side functions and components	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)		Yes Through the use of Python's mock library
Grouping Allows organizing tests in groups	Yes You can group your tests by annotating them as @PartOf a suite	N/A
Other Other useful information about the testing framework