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

	Testify https://github.com/Yelp/Testify	Nose https://nose.readthedocs.io/en/latest/
Programming language	Python	Python
Category	Unit Testing	Unit Testing, unittest Extensions
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	Nose is a Python unit test framework This is a Python unit test framework that intergrates well with doctests, unnittests, and 'no-boilerplate tests', that is tests written from scratch without a specific boilerplate.
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 nose is a unit testing tool which is very similar to unittest. It is basically unittest with extensions therefore just like unittest is can test front-end components and behaviour
Server-side Allows testing the bahovior of a server-side code	Yes Testify can test various server and database behaviours and functionality	Yes Nose can test back-end components and functionality as small units. One can write tests for each function that provides back-end functionality
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	Yes nose supports fixtures at the package, module, class, and test case levels, so that initialization which can be expensive is done as infrequently as possible.
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	Yes Group fixtures are allowed with nose, where a multitest state can be defined.
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.	Through use of third party libraries like test-generator and from the 'unittest.TestCase' library
Licence Licence type governing the use and redistribution of the software	Apache License 2.0	GNU Library or Lesser General Public License (LGPL) (GNU LGPL)
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 The nose library extends the built-in Python unittest module therefore has access to unittest.mock
Grouping Allows organizing tests in groups	Yes Testify includes support for detecting and running test suites, grouped by modules, classes, or individual test methods.	Yes With nose it collects tests automatically and there’s no need to manually collect test cases into test suites.
Other Other useful information about the testing framework