Autonomous Testing: Empower Your Testing Strategies

Tesena Fest workshop led by Ondřej Winter and Jan Beránek

19. 10. 2022 from 09:00 to 13:00 CET

About the Workshop

There are too many manual activities in testing automation (preparation, maintenance, evaluation…) High-skilled automation engineers are needed and usually leave into SW development by the time. Some critical visual tests need still human eyes. The autonomous testing approach applies AI/ML-based technologies to make the testing process independent of human intervention.

Learn how these techniques could help your teams and try out autonomous testing solutions:

  1. Autonomous assistants: add the superpowers for existing testing, increase coverage and stability
  2. Autonomous bot: cut the preparation time, learn to test like a human, and repair itself. Just provide the URL of the tested app.
Key Takeaways

Learn how to „just enable“ the autonomous testing platform and let it work for you

Find out how to integrate autonomous testing into your CI/CD pipelines

See what autonomy bot can discover in the chosen app without any scripting

See how easily can be superpowers added to any amount of existing automated tests in your beloved testing frameworks

Gain your own experience and figure out in which situations autonomy can help (and where to stick with traditional approaches)

Who Will Benefit

Testing professionals and their friends


Equipment Requirements

Browser (ideally Google Chrome) and an account on GitHub


Jan Beránek

Jan has always worked in teams that develop and supply software. He can do a lot, nothing properly, so most often he manages or advises, or both. He helps teams increase efficiency and especially quality. He likes the agile approach and innovative solutions.

Ondřej Winter

Ondřej is involved in testing for a year in Tesena. He deals mainly with automated and autonomous testing. As part of his doctoral studies, he participates in several projects dedicated to the numerical solution of fluid flow. His interests include numerical algorithms, machine learning, and neural networks.