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

Nose

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

Goconvey

https://github.com/smartystreets/goconvey
Programming language

Python

Go

Category

Unit Testing, unittest Extensions

Regression Testing, Unit 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.

BDD style testing framework for Go

Goconvey is a two pronged testing tool consisting of a test runner that watches your code for changes, runs 'go test' and renders your results in a web browser and the second a library that allows you to write BDD-style tests with standard 'go test' functions
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

Yes, Goconvey can perform front-end tests
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

Yes one can perform end-to-end HTTP tests with goconvey to see how an application works against remote servers
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.

Yes

Yes, Goconvey uses scopes to define fixtures and a reset function for teardown
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.

Yes

One can define group fixtures using scopes
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

Yes

The web UI has a button to open the built in generator
Licence
Licence type governing the use and redistribution of the software

GNU Library or Lesser General Public License (LGPL) (GNU LGPL)

Goconvey License

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

Using the mock package or mockery library to autogenerate mock code
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

Similar to a table driven approach an entire suite can be contained in a single function
Other
Other useful information about the testing framework