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

JBehave

https://jbehave.org/

Green

https://github.com/CleanCut/green
Programming language

Java

Python

Category

Acceptance Testing

Unit Testing

General info

JBehave is a Behaviour-Driven Development testing framework for java

JBehave is a Behaviour Driven Development framework. It intends to provide an intuitive and accessible way for automated acceptance testing

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 behaviour (scenarios) with JBehave

Yes

It can test front-end components of the django framework
Server-side
Allows testing the bahovior of a server-side code

JBehave tests scenarios and behaviours of components, it can test back-end behaviour

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

You have a few options for using fixtures in JBehave: you can run your steps before/after each scenario by using LifeCycle: you can use @BeforeStory and @AfterStory annotations or you can define a dummy scenario with your setup/teardown steps

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

You can define group fixtures with JBehave

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

BSD-style 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)

The best way to mock is to use third party libraries like Mockito, Jmock or Jmockit

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