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

JDave

http://jdave.org/

Nose

https://nose.readthedocs.io/en/latest/
Programming language

Java

Python

Category

Acceptance Testing

Unit Testing, unittest Extensions

General info

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.

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 behaviour can be tested with JDave

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

JDave can test server-side behaviour

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

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 integrates JMock 2 as mocking framework

Yes

The nose library extends the built-in Python unittest module therefore has access to unittest.mock
Grouping
Allows organizing tests in groups

Yes

Specifications can be grouped by tagging them with @Group annotation.

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