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

	Specter http://specter.sourceforge.net/	Python Testify https://github.com/Yelp/Testify
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	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 Developers can create specfications of the expected front-end behaviours and test them	Yes Front-end functionality and behaviour can be tested by Testify.
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 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	BSD 3-Clause 'New' or 'Revised' 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)	N/A	Yes It includes the turtle mock object library
Grouping Allows organizing tests in groups	Yes You can create your own test suites with specter	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