Smart AI Locators use Artificial Intelligence to recognize and adapt to changes to the elements in the UI so that tests are updated automatically.
User interface elements in web and mobile applications can move or change during a release which will cause tests to fail unless they are able to automatically adjust to these changes.
Many traditional automated testing frameworks and tools leave users to building static, brittle scripts. This is especially troublesome when multiple developers, testers, and engineers who are supposed to be benefitting from accurate test-driven feedback now have to deal with what looks like broken tests rather than delivering features and value to users.
In short, without using some form of intelligence element locators in your tests, you’re bound lose velocity to false-positives caused simply by changing your apps.
Smart AI Locators use Artificial Intelligence to recognize and adapt to changes to the elements in the UI so that tests are updated automatically. Leaders in the software testing industry are providing this feature as part of their product suite and organizations should consider their adoption for cost savings and release velocity.
AI-assisted test script element locators have several advantages over static element identifiers or page object models. AI can dynamically identify element locators, which means that it can adapt to changes in the application’s user interface without requiring manual updates to the test scripts. This is a significant advantage over static element identifiers, which need to be updated every time the application’s UI changes.
AI-assisted element locators can use advanced algorithms to identify the most efficient and reliable element locators. This is especially useful when dealing with dynamic web applications where the element IDs can change frequently, as AI can identify more robust identifiers based on the content of the element or its relationship to other elements on the page.
AI can learn from previous test runs and adapt its element locator strategy based on the results. This can lead to more efficient and effective test scripts, as AI can identify which element locators have worked well in the past and which ones need improvement.
Smart AI Locators are a key component of an overall ‘self-healing tests’ strategy, separate from trend-based outlier detection, retry thresholds and schedules, and root-cause (or contributing factors) automated analysis of environment issues correlated to test failures. However, Smart AI Locators help tests quickly adjust and develop healthy test run data series upon which these other capabilities can soundly recommend human interventions.
While AI can improve the efficiency and accuracy of element identification, it is important to not over-rely on it. Human oversight and quality assurance are still essential for ensuring the reliability and accuracy of test scripts.
Also, as with any AI system, there may be security concerns related to the collection and use of sensitive data. AI models used for element identification may collect and analyze sensitive user data, which may need to be secured properly. This is why having a solid test data management approach in place is critical, not only for all the reasons in traditional testing practices, but as AI further augments and accelerates testing cycles as a whole.
Overall, AI-assisted element locators can provide several advantages over traditional element identification methods, including greater flexibility, reliability, and accuracy. By leveraging the power of AI, testers can create more efficient and effective test scripts that can better handle the complex and dynamic nature of modern web applications.