Accelerate Test Automation through auto-healing using Power of AI

Adoption of test automation has its own challenges. A major challenge faced by the test automation engineer is increased maintenance due to on-going changes to the application user interface (UI). Changes to the UI makes automation scripts unusable and require a significant amount of effort to fix.  Due to shortening of development life cycles and the use of Test Automation in CI/CD pipelines, this problem has increased many fold. To minimize the maintenance efforts, it is imperative to have a test automation framework that can identify the object changes in the application and can take corrective actions automatically without any human intervention.  This will then ensure smooth script execution.

This auto-healing feature in the test automation framework can help resolve the above challenge. It addresses the challenge of object identification by automatically identifying the changed objects and with the help of baseline properties it predicts the closest match to the desired object by leveraging the power of Artificial Intelligence (AI). Thus, it ensures that the test script does not break even if one or more properties of the object gets changed. By using the auto-healing feature, manual script updates are no longer required if there is a change in the object identification properties. Scripts need to be modified only when there is a change in the application workflow or deletion/addition of an object. This significantly reduces the script maintenance efforts and allows the test automation team to focus on developing new scripts. It not only reduces the chances of reporting false positive defects but also makes automation scripts more dynamic to adapt to the changes in the application.

After executing numerous test automation projects and facing challenges of script maintenance, we at Celsior have designed a framework with a built-in auto-healing feature using AI. Our proprietary Object Manager utility captures the details of the objects and adds them to the object repository. Data captured in the object repository is then used to train the AI model. Once the AI model learns all the objects, it can predict the desired object that can be utilized by the test scripts. Test Automation makes a call to an AI engine in case the framework is not able to find an object using the traditional identification method. The AI engine predicts the closest match to the desired object and based on the framework performs the operation on the object. Once the execution of the scripts is complete, an HTML report is generated. By analyzing the execution report, a QE engineer can infer where AI came into action. Since AI may not always give 100% accurate results, QE engineers based on the results analysis can instruct the framework to update the latest properties in the object repository or to mark the test as failed.

In conclusion, Celsior’s AI-powered auto-healing test automation framework is a one stop testing solution for automating different kinds of testing needs on a wide range of applications and platforms. It also offers substantial benefits over traditional automation frameworks. By leveraging the power of artificial intelligence and machine learning, this innovative solution not only significantly reduces maintenance efforts but also ensures complete coverage of critical test scenarios with its auto-healing capabilities. The solution autonomously detects and addresses object changes in the application, leading to a significant 40-50 percent reduction in maintenance effort compared to traditional automation methods. The AI-powered auto-healing, test automation solution represents a game-changer in the field of software testing, helping teams to achieve higher levels of efficiency, reliability, and agility in their testing processes.  At the same time, it drives significant cost savings and accelerates time-to-market for software releases due to its integration with market-leading tools for CI/CD, test management, device cloud, communications, etc.


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Himanshu Gosain

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