Automatically Generate Your Unit Tests From Scratch With Pynguin

Summary Unit tests are an important tool to ensure the proper functioning of your application, but writing them can be a chore. Stephan Lukasczyk wants to reduce the monotony of the process for Python developers. As part of his PhD research he created the Pynguin project to automate the creation of unit tests. In this episode he explains the complexity involved in generating useful tests for a dynamic language, how he has designed Pynguin to address the challenges, and how you can start using it today for your own work. Announcements Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great. When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. 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Your host as usual is Tobias Macey and today I’m interviewing Stephan Lukasczyk about Pynguin, the PYthoN General UnIt test geNerator Interview Introductions How did you get introduced to Python? Can you describe what Pynguin is and the story behind it? What are the problems that Pynguin is designed to solve? What other projects are you drawing inspiration from? What are some of the use cases for automatic test generation? How is Pynguin implemented? What are the challenges that the dynamic nature of Python introduces? What are some of the packages and libraries that have been most helpful while building Pynguin? Can you talk through the workflow of using Pynguin to generate tests for a project? What are some of the limitations on what kinds of projects Pynguin can be used for? What are some design or implementation strategies in the code that you are generating tests for that will help make Pynguin’s job easier? Once a test suite has been created, what are the next steps? What are some of the initial assumptions or goals of the project that have been revised or challenged once you began implementing it? What are the most interesting, innovative, or unexpected ways that you have seen Pynguin used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Pynguin? When is Pynguin the wrong choice? What do you have planned for the future of Pynguin? Keep In Touch Related to Pynguin: best via GitHub Find me on Twitter Picks Tobias Concourse CI Stephan Cycling Take care of your health Closing Announcements Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at pythonpodcast.com/chat Links Pynguin University of Passau Passau, Germany Evosuite Hypothesis Podcast Episode Astor Walrus Operator MyPy Podcast Episode Pytest Podcast Episode UnitTest Bytecode library Pytype Monkeytype Podcast Episode Atheris from Google – coverage-guided fuzzing Blog series about “Python behind the scenes”: Ten thousand meters by Victor Skvortsov The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA