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

Green

https://github.com/CleanCut/green

Goconvey

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

Python

Go

Category

Unit Testing

Regression Testing, Unit Testing

General info

Green is a clean, colorful, fast Python test runner

This is a test runner that has pretty printing on output that makes results easy to read and understand. Some of its features include: Tests running in independent processes (fast), low redundancy in output (clean), supports pretty printing that is the terminal output, makes good use of color when the terminal supports it (colorful)

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

It can test front-end components of the django framework

Yes

Yes, Goconvey can perform front-end tests
Server-side
Allows testing the bahovior of a server-side code

Yes

It can test server-side behaviours of web applications written with Python

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

N/A

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.

N/A

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.

N/A

Yes

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

MIT License

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

Through the use of Python's mock library

Yes

Using the mock package or mockery library to autogenerate mock code
Grouping
Allows organizing tests in groups

N/A

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