"Developers like this kind of representation because that matches the internal model. That's how they organize the modules, how they place folders, and how they design object hierarchies. However, there might be multiple reasons why you don't have access to these internals. The development could be outsourced, you could be heavily integrating with third-party components, or to say it more bluntly, you might be vibe coding."
"Wouldn't it be better if you got a graphical representation of what is the intended accepted state and the state of the current version? The problem is that even for humans, it takes a while until you spot the difference. Once you discovered it, it pops out to you as a major flaw. The ideal world would be if an AI helps you flag that change and attribute it to an intended, an acceptable, or a faulty state."
Image-based test automation addresses testing scenarios where internal application states are inaccessible due to outsourced development, third-party integrations, or lack of code understanding. Traditional testing relies on internal representations like DOM or component trees, but visual testing through image processing algorithms offers an alternative approach. AI-powered image analysis can automatically detect differences between expected and actual application states, flagging changes and categorizing them as intended, acceptable, or faulty. This approach provides graphical representations that help testers understand test failures and application behavior without requiring deep code knowledge or access to internal structures.
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