(EN) Autonomous Visual Regression Testing 

There are still far too many manual activities in test automation. 

03. 03. 2022 from 10:30 CET

Marcel Veselka, Jan Beránek a Ondřej Winter

Webinar content

Current testing approaches are slowing down development and are unsustainable in the long term. We have long believed that test automation was the solution, but this belief was wrong. 

Nowadays we use test automation tools almost everywhere, but this testing approach is far too slow and requires strong technical skills that are hard to find and develop. Even if we do succeed in automating our tests, we are often faced with exhausting maintenance.

Is there a solution? Could it be autonomous testing?

Autonomous testing is the next phase in the evolution of testing approaches. We begin with several experiments to build an autonomous bot. In this talk, I will show you our first results from implementing autonomous visual regression testing using free and open-source tools.

Join us to find out more about this topic and to discuss your experience. 

Key takeaways

Learn more about autonomous testing

How autonomous tests could increase your testing efficiency

Get access to an open-source demo


03. 03. 2022 from 10:30 CET, online on Microsoft Teams


Marcel Veselka

Marcel has devoted his entire career to software testing and is currently working as a senior test consultant in Tesena.

He is a former vice president of CaSTB (ISTQB's regional office), co-founder of the tester community [pro]Test! and co-founder of Tesena. His interests include modern approaches to testing such as test automation, DevOps/Agile, autonomous testing, AI and machine learning

Jan Beránek

Honza has always worked in teams that develop and deliver software. He can do a lot, nothing properly, so most often he drives, advises, or both. He helps teams increase efficiency and especially quality. He likes a fast-moving approach and innovative solutions.

Ondřej Winter

Ondra, together with Tesena, has been testing for less than half a year. He mainly deals with automated and autonomous testing. As part of his doctoral studies, he participates in several projects devoted to the numerical solution of flowing liquids.

His interests include numerical algorithms, machine learning, neural networks.