Knapsack Pro

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