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

Lettuce

https://pypi.org/project/lettuce/

Selenium

https://pypi.org/project/selenium/
Programming language

Python

Python

Category

Unit Testing, Acceptance Testing

Web Automation

General info

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

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
xUnit
Set of frameworks originating from SUnit (Smalltalk's testing framework). They share similar structure and functionality.

No

However It can generate xml results for behaviour tests xUnit style

No

Client-side
Allows testing code execution on the client, such as a web browser

Yes

By integrating Lettuce with Selenium’s Python bindings, you have a robust framework for testing Django applications. It can test front-end behaviour

Yes

It is primarily a browser automation tool which tests front-end components and functionality
Server-side
Allows testing the bahovior of a server-side code

Yes

Lettuce can test various server and database behaviours and interactions

Yes

It can perform Unit tests and can test various components and behaviours in the backend using a BDD or TDD approach
Fixtures
Allows defining a fixed, specific states of data (fixtures) that are test-local. This ensures specific environment for a single test

N/A

Yes

By writing your Selenium WebDriver tests in PyTest, this gives you access to Pytest's powerful fixture model
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

Yes

One can group fixtures if accessing Pytest's fixture model
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 third party library

Yes

By using a library such as Faker or Fake-factory
Licence
Licence type governing the use and redistribution of the software

Unknown

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)

By adding the lettuce-tools library one has access to the Mock module to implement a configurable http REST mock.

Yes

It includes support for mocking
Grouping
Allows organizing tests in groups

Yes

It allows grouping of tests

Yes

By using the TestNG feature with which we can create groups and maintain them easily
Other
Other useful information about the testing framework