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

Green

https://github.com/CleanCut/green

NBi

http://www.nbi.io/
Programming language

Python

.NET

Category

Unit Testing

Integration Testing, Unit Testing, Acceptance 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)

NBi is an open-source framework for testing Business Intelligence solutions or validating data quality.

NBi helps you to create tests targeting your databases, cubes, etls and reports. Tests are written in xml using an intuitive syntax therefore thereis no need of any development language. Nbi tests target databases, cubes, etls and reports
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

No

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

Nbi tests Business intelligence software which retrieve, analyze, transform and report data therefore it targets databases, cubes, etls and reports and you can natively connect to any database supporting OleDb or ODBC connection
Fixtures
Allows defining a fixed, specific states of data (fixtures) that are test-local. This ensures specific environment for a single test

N/A

No

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

No

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

Apache License 2.0

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

Yes

You can create your own mock objects
Grouping
Allows organizing tests in groups

N/A

Yes

Yes, Nbi comes with a solution to automate, as much as possible, the creation of the test-suites through its user interface, named GenBI
Other
Other useful information about the testing framework