Categorization of Smart Test Failure with AI

The team spent hours sorting the failure of the test, finding out whether it was a real bug, scaly tests, environmental problems, or just noise. This manual process is slow, inconsistent, and inefficient. To speed up the process of sorting the test failure, we have introduced the AI ​​categorization feature of failure to our test intelligence platform.

Built into the most slow insight, this feature automatically analyzes and categorizes test failures based on patterns, environment, browsers, and operating systems. This helps ensure that test failures are handled more consistently and reduce the need for manual triaging.

To learn more, go to the documentation about the categorization of AI failure in test intelligence.

What is AI categorization failure?

AI feature of failure categorization in test intelligence allows you to automatically categorize test failures based on parameters such as the environment, browser, OS, and type of failure. This learns from your input to make the test automation more efficiently and reduce manual triage time.

This represents a shift in the management failure management, moving from reactive manual categorization to intelligent and intelligent classifications that grow smarter with every interaction. AI feature of failure categorization not only classifies failures; This understands it in the context of your specific testing, recognizing patterns that will be difficult for human examiners to identify in thousands of running tests.

The main feature of AI Failure Categorization

The AI ​​Categorization Failure feature is equipped with features designed to streamline your testing workflow.

This is what makes it stand out from the traditional categorization approach:

• Introduction to Advanced Patterns: AI failure categorization features analyze details such as browser type, operating systems, environmental arrangements, error messages, and when failure occurs to detect patterns in various running tests.
• Failure trend analysis: This gives a clear dashboard that breaks down failure based on categories from time to time, helping you find recurrent problems and monitor progress in your testing efforts.
• Smart notice and stomach: This feature automatically provides a type of failure to the right team, sending product bugs to developers, environmental problems to Devops, and tests the problem of scripts to QA engineers.
• Historical Pattern Analysis: This stores notes on past failure patterns, making it easier to recognize long -term trends and identify more and more sustainable problems in your testing environment.
• Time Save Automation: New failures are automatically categorized in seconds, allowing your team to focus on fixing the problem instead of spending time to sort it manually.

Conclusion

We in Lambdatest understand that efficient failure management is very important to maintain the speed of development and product quality. The AI ​​Failure Categorization feature is a significant leap in making the test automation is really smart. The more you use it, the smarter for your specific testing environment.

Have a question about getting the maximum results from the AI ​​Failure Categorization feature? Our support team is ready to help you optimize your failure management workflow. Reach through the chat option on your dashboard or our email at support@lambdatest.com.

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