Apps break. It’s what they do. Everyone has seen skewed layouts, missing buttons, and overlapping text. Those visual bugs are such a pain because traditional test automation usually can’t catch them. However, they cause serious reputational risk because appearances matter. The types of problems scripts can catch usually require complicated element locators and assertions, too. The best way to catch visual problems is to look at them with human eyes. People are good at quickly noticing things that don’t look right.
If we can train an AI model to look for important visual differences between app snapshots, then we can automate visual testing! In this talk, I’ll show how how to apply AI-backed visual comparisons to end-to-end test automation. We’ll transform traditional tests into much simpler scenarios that save time for both development and execution. You’ll see how to make visual comparisons between baselines and updated snapshots. A picture is truly worth a thousand assertions. Ultimately, visual testing like this enables you to spend more time on proper test coverage and less time on automation implementation!
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