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

	stestr https://pypi.org/project/stestr/	Lettuce https://pypi.org/project/lettuce/
Programming language	Python	Python
Category	Unit Testing	Unit Testing, Acceptance Testing
General info	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.	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 Stestr being a test runner that runs unittest tests, it can test fron-tend functionality and behaviour.	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 Stestr being a test runner that runs unittest tests, it can run back-end tests for functionality and behaviour.	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 use of a third party library like Fixture	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.	By use of a third party library like Fixture	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 like test-generator	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)	N/A	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	N/A	Yes It allows grouping of tests
Other Other useful information about the testing framework