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

	Python Testify https://github.com/Yelp/Testify	NaturalSpec https://www.nuget.org/packages/NaturalSpec/
Programming language	Python	.NET
Category	Unit Testing	Unit Testing
General info	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	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 Front-end functionality and behaviour can be tested by Testify.	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 Testify can test various server and database behaviours and functionality	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	Yes Fixture methods are supported and it follows a decorator based approach, that is they are written similar to decorators	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 fixtures 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.	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.	N/A
Licence Licence type governing the use and redistribution of the software	Apache License 2.0	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 It includes the turtle mock object library	Yes Mocks are available through third party libraries like Moq
Grouping Allows organizing tests in groups	Yes Testify includes support for detecting and running test suites, grouped by modules, classes, or individual test methods.	N/A
Other Other useful information about the testing framework