Python Testify vs Wallaby.js comparison of testing frameworks
What are the differences between Python Testify and Wallaby.js?

	Python Testify https://github.com/Yelp/Testify	Wallaby.js https://wallabyjs.com/
Programming language	Python	JavaScript
Category	Unit Testing	Unit Testing
General info	A Python unit testing framework modelled after unittest Testify is modelled after unittest but has more features while still supporting unittest classes. It has more pythonic naming conventions, an better test runner output visually, a decorator-based approach to fixture methods among many other features	Wallaby.js is an integrated continuous testing tool for JavaScript. Wallaby.js runs your tests immediately as you change your code (you don’t even have to save the file) and reports code coverage and other results directly to your code editor. Its also uses various techniques such as dependency analysis to only execute tests affected by your code changes and parallel test execution to ensure your tests run as fast as possible.
xUnit Set of frameworks originating from SUnit (Smalltalk's testing framework). They share similar structure and functionality.	No	N/A
Client-side Allows testing code execution on the client, such as a web browser	Yes Front-end functionality and behaviour can be tested by Testify.	Yes Wallaby.js can test front-end components and functionality
Server-side Allows testing the bahovior of a server-side code	Yes Testify can test various server and database behaviours and functionality	Yes Wallaby.js can test server behaviour and functionality
Fixtures Allows defining a fixed, specific states of data (fixtures) that are test-local. This ensures specific environment for a single test	Yes Fixture methods are supported and it follows a decorator based approach, that is they are written similar to decorators	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.	Yes Group fixtures are supported	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.	Yes One can create generator methods to yield runnable test methods which will pick out the test methods from your TestCases, and then exclude any in any of your exclude_suites method.If there are any require_suites, it will then further limit itself to test methods in those suites.	N/A
Licence Licence type governing the use and redistribution of the software	Apache License 2.0	N/A
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 includes the turtle mock object library	By use of a third party library Jest works well with wallaby
Grouping Allows organizing tests in groups	Yes Testify includes support for detecting and running test suites, grouped by modules, classes, or individual test methods.
Other Other useful information about the testing framework