Step by step test execution details so you can share, recreate, and resolve issues

Improve communication between developers and testers by using a shared platform for test results.

Errors, JavaScript exceptions, and logs are displayed and sorted by the step at which each event occurred in the test.

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Screenshots before and after every step

Screenshots are taken before and after each action so you can see the state of the application at every step. The actions taken are highlighted so you can easily follow mabl through the journey.

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Catch unexpected changes in your application UI

Using the visual explorer, any member of your team can quickly browse screenshots from all journey runs to detect important visual regressions that typically slip through the cracks. Teams also use the explorer to validate expected visual changes and perform exploratory visual testing.

For example, want to see screenshots for a given URL path and date? Just apply a filter.

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Side-by-side screenshot comparisons

Baseline screenshots captured from previous test runs are displayed next to screenshots from the latest test run. Visual changes are labeled as warnings in the test output so you can quickly filter for them. Changes are highlighted with bounding boxes for easy review.

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Visual Explorer

Compare versions of your application step by step in mabl.

Create your own visual baseline to compare your app's current state against.

Quickly find visual regressions on any URL in your application.

See DOM Snapshots and HAR Captures of your application.

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Know when there are performance regressions

Test execution and page load times are tracked for every test.

Machine learning models help mabl differentiate between anomalies and significant slowdowns to build tailored thresholds.

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Test results, output, and performance data - all in one place

Performance tracking is built into your tests so you can report and fix regressions fast.

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Speed Index - Using Google Lighthouse

mabl's Speed Index uses Google Lighthouse metrics to indicate how users are likely to perceive the load speed of your application's UI for more refined insights on page load times.

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Intelligently tracked performance trends

Expected behavior, anomalies, and significant slowdowns are identified using machine learning models to reduce noise and more accurately track performance trends.

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