DEVOPSdigest

Katalon announced the general availability of Katalon Visual Testing.

The software testing tool verifies that user interfaces will appear correctly to all, ensuring that each element on a web page or mobile app functions perfectly and appears in the right shape, size and position - regardless of the device and browser. Ready to use out-of-the-box, Katalon Visual Testing works without the need to use third-party tools or configurations to implement visual testing into work pipelines for test authoring, execution and review.

In addition to the standard approach of comparing differences pixel by pixel, Katalon Visual Testing enables software quality teams to automatically analyze, differentiate, and identify valid visual UI differences from minor visual infractions through two AI image comparison options:

- Layout-based testing: Instead of identifying where individual pixels are not the same, layout-based AI Visual Testing analyzes the layout of images and visual objects in relation to each other and compares those relative differences between baseline and test results. When an image, object, or line of text differs by only a few pixels or lines, a standard visual test flags the difference as a failure. By contrast, Katalon AI Visual Testing can intelligently deduce when there is only a minor difference in the layout to help testers efficiently decide if quality tests pass or fail, significantly reducing false positives and unnecessary manual reviews.

- Content-based testing: This is the better option for when visual quality is rated by the completeness and accuracy of text content rather than the layout and existence of images and UI elements. With no regard to font, layout, color, or changes in its background, Content-based AI Visual Testing prioritizes the review and validation of text within a UI. As with the layout-based comparison, this testing also increases the efficacy of content testing, and reduces manual validation efforts by hundreds and even thousands of hours per year.

"Automated functional tests are helpful in detecting functionality changes resulting from recent code changes, but they cannot detect visual changes to the application such as layout, colors, fonts, and misplacement of elements," said Katalon CTO Coty Rosenblath. "Human-based visual regression testing is possible but it is error prone and requires a huge effort considering the large number of pages and elements that modern applications contain. With Katalon AI-based Visual Testing, quality testing efforts are faster and more accurate, reducing tester hours by 99% compared to manual testing."