Katalon AI Visual Testing General Availability

Over the past few months, we have slowly rolled out and heighten our AI Visual Testing resolution during the trial period. This month we are majestic to announce the general availability (GA) of Katalon AI Visual Testing that include extra enhancements. & nbsp; & nbsp;

The following reviews all the unite AI Visual Testing characteristic in the GA. & nbsp;

The Need for Visual Testing

Mod businesses bet on digital experiences for both customers and business users. Modernistic test automation helps to validate functionality within these digital experiences, but validating the UI/UX keep to be an intense process of:

• Manually, or through automation, capture image & nbsp;
• Manually, or through partial automation, comparing for differences & nbsp; & nbsp;

While tedious and time-consuming, failure to include visual testing as constituent of your quality operation could lead to:

• Bad first impressions due to variations or broken UI layouts, such as buttons and/or text that are unintentionally displace
• Loss of sales afford specify or no operable UI Interactions due to non-visible ingredient on pages that functional mechanization test can not assert
• Frustration or ire triggered by wrong use of language that somehow slew through various review rounds, etc.

All of these are actual problems that have impacted businesses in real life, lead in & nbsp; thousands or millions of dollar in lost revenue, client dissatisfaction, and negative brand image.

Optical Testing and AI

Automated functional tests are helpful in ensuring software behaves as expected with any late code changes, but they can not detect visual modification to the application such as layout, colors, fonts, misplacement of elements, unexpected optical overlays, etc. Visual verifications can be done with the bare eye, but the effort needed will quickly swamp the available capability of all squad, regard the typical large count of pages and elements that modern applications moderate. This method of manual visual testing is also error-prone since there is a high probability to lose detail during mundane, insistent tasks.

Automated Ocular examination is an alternative puppet that enables team to make optical fixation tests with minimal effort. Basic pixel-to-pixel image comparing can be automated to help filter through the hundreds or K of persona that need to be manually compared. Unfortunately, this often still leaves hundreds of images that fail since pixel-based testing can generate failed tests even when the visual difference isn ’ t noticeable by the user. & nbsp; & nbsp;

Katalon has offered pixel-based optic examination for a long time now and has heed to customer ' feedback to create a new visual examination operation that leverages AI to add an additional automated layer of image comparisons to further filter out false positives on visual examination. Its seamless integration with the Katalon platform means that users can start using it out of the box without have to worry about complex setup or third-party contour.

 

AI Visual Testing

Pro tip: Tools like SUSA can handle this autonomously — upload your app and get results without writing a single test script.

AI Visual Testing has get in TestOps. Katalon ’ s new AI expands on our existing pixel comparing visual testing with two additional AI-enabled visual tests: content-based and layout-based. These optical testing options greatly improve the speed and efficaciousness of UI quality testing. They allow package quality squad to automatically analyze, separate, and identify valid visual UI differences from minor visual misdemeanor, reducing manual optical validation efforts by hundreds or 1000 of hour per year. & nbsp;

Layout-Based AI Visual Testing

Instead of but identifying when item-by-item pixels are not the same, layout-based AI Visual Testing analyzes the layout of images and ocular objects in coitus to each other and equate those relative conflict between baseline and examination results. When the relative differences are substantial, the test fails. However, when an persona, object, or line of the textbook differs by only a few pixels or line, where a standard visual test would flag a failure, AI Visual Testing intelligently deduces that there is only a minor difference in layout and surpass the test. This strategy significantly reduces false positives and wasted manual review effort.

Content-Based AI Visual Testing

When visual quality is grade by the completeness and accuracy of text content rather than the layout and world of images and UI elements, content-based AI Visual Testing is the best option. Content-based AI Visual Testing prioritizes the follow-up and validation of text within a UI regardless of font, layout, color, or changes in its background. Like the layout-based comparing, this also increases the efficacy of content testing and reduces manual proof exertion by century or thousands of hours per year.

Support Multiple Visual Baseline Collections

Different test cases/test suit cover different parts of the application, hence they need different sets of baseline images during visual examination. The exploiter can now choose to use different baselines when scheduling a test run.

You may view baseline collections as well as their images via the “ Visual Testing ” tab. & nbsp;

Ignore Zones in Visual Tests

Many UIs now contain dynamical content such as ads that make visual testing more complicated. Katalon ’ s Visual Testing now lets you create ignored zones within baseline ikon to prevent difference in these dynamically render UI element from triggering failed tests. This is a powerful new feature that addresses one of the main challenges of optical testing.

[NEW] –Apply/Delete Ignored Zone (s) for All Images in a Collection

For use cases where the region (s) that need to be ignored exist in all the baseline visualise, this liberation lend the power to apply and erase ignored zones to all baseline images in a collection. This offer a big-quality-of-life improvement for testers that see this use cause frequently.

[NEW] - Specify Baseline Collection ID as a Parameter in Katalon Command

For users fulfill visual tests directly from the CI/CD pipeline using Katalon Command, a new parameter calledtestOps.baselineCollectionIdhas been added to the dictation line, which triggers the visual exam run. Users can either input an existing baseline solicitation ID to the parameter or stimulus -1 to the parameter to generate a new baseline collection.

TestOps Enhancements

Uncompleted Status in Test Runs

Prior to this release,Test Run status in TestOps could only bePassed or Failed. This was inaccurate for test runs that were canceled or those that were even scat or importing. Now test tally that receive be offset or are still running are placed in theIncomplete status.

For a complete list of the new features, improvement, and fixes, please see the.

In Case You Missed It

• Check out this and other to review previous TestOps and Katalon product release.
• Learn more about TestOps and how it helps you with full visibility, speed, and efficiency.
• Visit the. & nbsp;

As always, delight post any questions, ideas, or concern in our. We are eager to hear from you. & nbsp;