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

	Ginkgo http://onsi.github.io/ginkgo/	Nose https://nose.readthedocs.io/en/latest/
Programming language	Go	Python
Category	Unit Testing, Intergration Testing	Unit Testing, unittest Extensions
General info	BDD testing framework for Go Ginkgo is a BDD testing framework for Go that has a great matcher library to go with it called Gomega and intergrates with the standard testing library	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, by creating unit tests then testing individual 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 by creating unit tests then testing various back-end components	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.	Yes They are available by running the command: 'ginko bootstrap'	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 Dvelopers can generate mocks by using the third party package 'gomock'	Yes The nose library extends the built-in Python unittest module therefore has access to unittest.mock
Grouping Allows organizing tests in groups	Yes Ginkgo allows you to group tests in 'Describe' and 'Context' container blocks. It also provides 'It' and 'Specify' blocks to hold your assertions	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