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

	Lettuce https://pypi.org/project/lettuce/	unexpected http://unexpected.js.org/
Programming language	Python	JavaScript
Category	Unit Testing, Acceptance Testing	Unit Testing
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	An extensible BDD assertion toolkit Unexpected is an extensible BDD assertion toolkit that is compatible with all test frameworks,is Node.js ready (require('unexpected')) and supports asynchronous assertions using promises among other features. It can be used with any test runner that catches exceptions, but the developer recommends Mocha, Jest or Jasmine as they are integrated tested with every release
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	N/A
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 Unexpected can be used in a browser environment to test 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 Unexpected is used in a Node.JS environment to 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	N/A	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.	N/A	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 third party library	N/A
Licence Licence type governing the use and redistribution of the software	Unknown	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)	By adding the lettuce-tools library one has access to the Mock module to implement a configurable http REST mock.	N/A
Grouping Allows organizing tests in groups	Yes It allows grouping of tests	N/A
Other Other useful information about the testing framework