Selenium vs JDave comparison of testing frameworks
What are the differences between Selenium and JDave?

	Selenium https://pypi.org/project/selenium/	JDave http://jdave.org/
Programming language	Python	Java
Category	Web Automation	Acceptance Testing
General info	Selenium is an open source tool used to test web applications Selenium is a powerful testing tool which can send standard Python commands to different browsers, despite variations in browser design. It also provides extensions to emulate user interaction with browsers, a distribution server for scaling browser allocation, and the infrastructure for implementations of the W3C WebDriver specification that lets you write interchangeable code for all major web browsers	JDave is a BDD framework for Java JDave is inspired by RSpec and integrates JMock 2 as mocking framework and Hamcrest as matching library. It uses JUnit adapter to launch JDave specifications. This way it is possible to have IDE, build tool and coverage tool support from day one.
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 is primarily a browser automation tool which tests front-end components and functionality	Yes Front-end behaviour can be tested with JDave
Server-side Allows testing the bahovior of a server-side code	Yes It can perform Unit tests and can test various components and behaviours in the backend using a BDD or TDD approach	Yes JDave can test server-side behaviour
Fixtures Allows defining a fixed, specific states of data (fixtures) that are test-local. This ensures specific environment for a single test	Yes By writing your Selenium WebDriver tests in PyTest, this gives you access to Pytest's powerful fixture model	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 One can group fixtures if accessing Pytest's fixture model	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 By using a library such as Faker or Fake-factory	N/A
Licence Licence type governing the use and redistribution of the software	Apache License 2.0	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 It includes support for mocking	Yes It integrates JMock 2 as mocking framework
Grouping Allows organizing tests in groups	Yes By using the TestNG feature with which we can create groups and maintain them easily	Yes Specifications can be grouped by tagging them with @Group annotation.
Other Other useful information about the testing framework