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

	Testify https://github.com/stretchr/testify	Nose https://nose.readthedocs.io/en/latest/
Programming language	Go	Python
Category	Unit Testing	Unit Testing, unittest Extensions
General info	A set of golang packages that has many tools for testing Go code Testify is a Go testing framework that has some great features like easier assertions, Test suite Interfaces, and Mocks	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 Yes, since it is also easily hooked to 'testing' package it is used to test front-end components	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 Yes it can also be used to test back-end components 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	N/A	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.	N/A	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.	N/A	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	MIT License	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 Its 'mock' package has a mechanism for easily writing mock objects that are used in place of real objects	Yes The nose library extends the built-in Python unittest module therefore has access to unittest.mock
Grouping Allows organizing tests in groups	Yes Using the 'suite' package developers can build a test suite as a struct build teardown and setup methods as well as testing methods on the struct then run them with 'go test'	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