Specter vs pytest comparison of testing frameworks
What are the differences between Specter and pytest?

	Specter http://specter.sourceforge.net/	pytest https://docs.pytest.org/en/latest/
Programming language	.NET	Python
Category	Acceptance Testing	Unit Testing
General info	Specter is a behaviour-driven development framework for .NET and Mono Specter enables behavior driven development (BDD) by allowing developers to write executable specifications for their objects, before actually implementing them, this is similar to test driven development however the different nomenclature makes it different from writing 'tests' for code that does not exist yet	Pytest is the TDD 'all in one' testing framework for Python Pytest is a powerful Python testing framework that can test all and levels of software. It is considered by many to be the best testing framework in Python with many projects on the internet having switched to it from other frameworks, including Mozilla and Dropbox. This is due to its many powerful features such as ‘assert‘ rewriting, a third-party plugin model and a powerful yet simple fixture model.
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 Developers can create specfications of the expected front-end behaviours and test them	Yes pytest can test any part of the stack including front-end components
Server-side Allows testing the bahovior of a server-side code	Yes Yes developers can create specfications of the expected back-end behaviours and test these.	Yes pytest is powerful enough to test database and server components 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 Pytest has a powerful yet simple fixture model that is unmatched in any other testing framework.
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 Pytest's powerful fixture model allows grouping of fixtures
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 pytest has a hook function called pytest_generate_tests hook which is called when collecting a test function and one can use it to generate data
Licence Licence type governing the use and redistribution of the software	BSD 3-Clause 'New' or 'Revised' 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)	N/A	Yes By either using unittest.mock or using pytest-mock a thin wrapper that provides mock functionality for pytest
Grouping Allows organizing tests in groups	Yes You can create your own test suites with specter	Yes Tests can be grouped with pytest by use of markers which are applied to various tests and one can run tests with the marker applied
Other Other useful information about the testing framework