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

	Nose https://nose.readthedocs.io/en/latest/	JDave http://jdave.org/
Programming language	Python	Java
Category	Unit Testing, unittest Extensions	Acceptance Testing
General info	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.	JDave is a BDD framework for Java JDave is inspired by RSpec and integrates JMock 2 as mocking framework and Hamcrest as matching library. It uses JUnit adapter to launch JDave specifications. This way it is possible to have IDE, build tool and coverage tool support from day one.
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 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	Yes Front-end behaviour can be tested with JDave
Server-side Allows testing the bahovior of a server-side code	Yes Nose can test back-end components and functionality as small units. One can write tests for each function that provides back-end functionality	Yes JDave can test server-side behaviour
Fixtures Allows defining a fixed, specific states of data (fixtures) that are test-local. This ensures specific environment for a single test	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.	N/A
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 allowed with nose, where a multitest state can be defined.	N/A
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.	Through use of third party libraries like test-generator and from the 'unittest.TestCase' library	N/A
Licence Licence type governing the use and redistribution of the software	GNU Library or Lesser General Public License (LGPL) (GNU LGPL)	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)	Yes The nose library extends the built-in Python unittest module therefore has access to unittest.mock	Yes It integrates JMock 2 as mocking framework
Grouping Allows organizing tests in groups	Yes With nose it collects tests automatically and there’s no need to manually collect test cases into test suites.	Yes Specifications can be grouped by tagging them with @Group annotation.
Other Other useful information about the testing framework