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

Green

https://github.com/CleanCut/green

NaturalSpec

https://www.nuget.org/packages/NaturalSpec/
Programming language

Python

.NET

Category

Unit Testing

Unit 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)

NaturalSpec is a .NET Unit testing framework

NaturalSpec is a .NET UnitTest framework which provides automatically testable specs in natural language. NaturalSpec is based on NUnit and completely written in F# - you don't have to learn F# to use it.
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 test front-end components with NaturalSpecit. It is a Unit testing framework therefore you can test front-end modules and classes independently
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

You can test back-end components with NaturalSpec. It is a Unit testing framework therefore you can test back-end modules and classes independently
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

Yes

Mocks are available through third party libraries like Moq
Grouping
Allows organizing tests in groups

N/A

N/A

Other
Other useful information about the testing framework