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

	Selenium https://pypi.org/project/selenium/	Lettuce https://pypi.org/project/lettuce/
Programming language	Python	Python
Category	Web Automation	Unit Testing, 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	Lettuce is a BDD testing tool for Python Lettuce is a testing tool for Python which is inspired by Ruby's Cucumber that supports Gherkin. It can execute plain-text functional descriptions as automated tests for Python projects just like Cucumber does for Ruby
xUnit Set of frameworks originating from SUnit (Smalltalk's testing framework). They share similar structure and functionality.	No	No However It can generate xml results for behaviour tests xUnit style
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 By integrating Lettuce with Selenium’s Python bindings, you have a robust framework for testing Django applications. It can test front-end behaviour
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 Lettuce can test various server and database behaviours and interactions
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	Yes By using a third party library
Licence Licence type governing the use and redistribution of the software	Apache License 2.0	Unknown
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	By adding the lettuce-tools library one has access to the Mock module to implement a configurable http REST mock.
Grouping Allows organizing tests in groups	Yes By using the TestNG feature with which we can create groups and maintain them easily	Yes It allows grouping of tests
Other Other useful information about the testing framework