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

	Lettuce https://pypi.org/project/lettuce/	stestr https://pypi.org/project/stestr/
Programming language	Python	Python
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	stestr is a Python test runner designed to execute unittest test suites stestr executes unittest test suites by using multiple processes to split up execution of a test suite then stores a history of all test runs to help in debugging failures and optimizing the scheduler to improve speed.
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 Stestr being a test runner that runs unittest tests, it can test fron-tend functionality and behaviour.
Server-side Allows testing the bahovior of a server-side code	Yes Lettuce can test various server and database behaviours and interactions	Yes Stestr being a test runner that runs unittest tests, it can run back-end tests for functionality and behaviour.
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 use of a third party library like Fixture
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	By use of a third party library like Fixture
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 like test-generator
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.	N/A
Grouping Allows organizing tests in groups	Yes It allows grouping of tests	N/A
Other Other useful information about the testing framework