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

	stestr https://pypi.org/project/stestr/	NaturalSpec https://www.nuget.org/packages/NaturalSpec/
Programming language	Python	.NET
Category	Unit Testing	Unit Testing
General info	stestr is a Python test runner designed to execute unittest test suites stestr executes unittest test suites by using multiple processes to split up execution of a test suite then stores a history of all test runs to help in debugging failures and optimizing the scheduler to improve speed.	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 Stestr being a test runner that runs unittest tests, it can test fron-tend functionality and behaviour.	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 Stestr being a test runner that runs unittest tests, it can run back-end tests for functionality and behaviour.	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 By use of a third party library like Fixture	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.	By use of a third party library like Fixture	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 By using a library like test-generator	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)	N/A	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