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

Green

https://github.com/CleanCut/green

Google Puppeteer

https://developers.google.com/web/tools/puppeteer
Programming language

Python

JavaScript

Category

Unit Testing

Browser Automation

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)

Puppeteer is a Node library which provides browser automation for chrome and chromium

Puppeteer runs headless by default, but can be configured to run full (non-headless) Chrome or Chromium; It provides a high-level API to control Chromium or Chrome over the DevTools Protocol
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

Most things you can do manually in the browser can be done using puppeteer, therefore you can create a testing environment for your tests to run directly. You can test front-end functionality such as UI testing with puppeteer
Server-side
Allows testing the bahovior of a server-side code

Yes

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

No

Fixtures
Allows defining a fixed, specific states of data (fixtures) that are test-local. This ensures specific environment for a single test

N/A

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.

N/A

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.

N/A

No

Licence
Licence type governing the use and redistribution of the software

MIT License

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

Through the use of Python's mock library

No

Grouping
Allows organizing tests in groups

N/A

No

Other
Other useful information about the testing framework