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

	NaturalSpec https://www.nuget.org/packages/NaturalSpec/	Python Testify https://github.com/Yelp/Testify
Programming language	.NET	Python
Category	Unit Testing	Unit Testing
General info	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.	A Python unit testing framework modelled after unittest Testify is modelled after unittest but has more features while still supporting unittest classes. It has more pythonic naming conventions, an better test runner output visually, a decorator-based approach to fixture methods among many other features
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 components with NaturalSpecit. It is a Unit testing framework therefore you can test front-end modules and classes independently	Yes Front-end functionality and behaviour can be tested by Testify.
Server-side Allows testing the bahovior of a server-side code	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	Yes Testify can test various server and database behaviours and functionality
Fixtures Allows defining a fixed, specific states of data (fixtures) that are test-local. This ensures specific environment for a single test	N/A	Yes Fixture methods are supported and it follows a decorator based approach, that is they are written similar to decorators
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	Yes Group fixtures are supported
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	Yes One can create generator methods to yield runnable test methods which will pick out the test methods from your TestCases, and then exclude any in any of your exclude_suites method.If there are any require_suites, it will then further limit itself to test methods in those suites.
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 Mocks are available through third party libraries like Moq	Yes It includes the turtle mock object library
Grouping Allows organizing tests in groups	N/A	Yes Testify includes support for detecting and running test suites, grouped by modules, classes, or individual test methods.
Other Other useful information about the testing framework