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

SOAtest

https://www.parasoft.com/products/soatest

Green

https://github.com/CleanCut/green
Programming language

JavaScript

Python

Category

Functional Testing, Intergration Testing

Unit Testing

General info

It's a web based service platform. Script-less REST and SOAP API testing, UI testing, load/performance, and security testing that’s easy to use.

Parasoft SOAtest brings artificial intelligence and machine learning to functional testing, to help users test applications with multiple interfaces (UI, REST & SOAP APIs, web services, microservices, and more), simplifying automated end-to-end testing (databases, MQ, JMS, EDI, or even things like Kafka). Unlike any other API testing tool, Parasoft SOAtest mitigates the cost of re-work by proactively adjusting your library of tests as services change.

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)
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

SOAtest is a UI and API testing framework that tests front-end functionality by capturing user interactions directly in the browser without requiring any scripting

Yes

It can test front-end components of the django framework
Server-side
Allows testing the bahovior of a server-side code

No

Yes

It can test server-side behaviours of web applications written with Python
Fixtures
Allows defining a fixed, specific states of data (fixtures) that are test-local. This ensures specific environment for a single test

No

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.

No

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.

No

N/A

Licence
Licence type governing the use and redistribution of the software

N/A

MIT 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)

No

Yes

Through the use of Python's mock library
Grouping
Allows organizing tests in groups

No

N/A

Other
Other useful information about the testing framework