Artificial Intelligence (AI) and Machine Learning (ML) have transformed almost every sector. The testing industry is no longer an exception to this. It hasn’t been long, we used to discuss the importance of “continuous testing” for “agile” and “DevOps”. Undoubtedly, continuous testing provides the path for swiftly embedding quality assurance by ensuring that changes in the code can be integrated efficiently in the DevOps. However, continuous testingis not a walk in the park due to factors like siloed automation, lack of end-to-end visibility of requirements, high volume tests, etc. This is the time to incorporate Artificial Intelligence (AI) & Machine Learning (ML) to enable an autonomous and zero-touch QA.
Why is AI for Continuous Testing?
With releases taking place on a weekly basis and updates are rolling out on almost every alternate day, there is a need to streamline software testing by making it smarter and more efficient. Incorporating AI will make the testing process smarter since QA teams can trigger unattended test cycles in which defects can be identified based on insights that are picked from historical data sets and past events. AI-driven algorithms can completely mimic human intelligence while ML automatically updates the test scripts, eliminating unstable test cases. AI-based engines can ensure that only a robust code progresses from one stage to the next and ML-specific algorithms extract patterns by accessing data to make predictions. All this will help in improving software testing.
Given below are some of the scenarios that manifest how AI is about to change testing
Automated Visual Testing for UI –Visual validation is all about ensuring that UI is appearing correctly to the users. It is really difficult to detect discrepancies while testing colour, position, & size of the UI elements while testing manually. AL-based visual testing is all about recognizing the patterns. With AI, it can be ensured that UI appears correctly to the users and UI elements are not overlapped. In nutshell, AI & ML-based tests automatically detect all the visual bugs to validate the visual correctness of the apps.
API Testing –API has taken the central stage of development. However, creating a multitude of scenarios to test API while ensuring coverage is difficult since it requires an understanding of the API. With AI-driven test automation, this problem can be addressed efficiently since AI can identify patterns and connections between API calls and group the scenarios to deliver adequate test coverage.
Test Maintenance – Test stability of apps that feature “dynamic” elements often gets compromised with updates. When changes are made to the app directly in the form of new screens, buttons, or user flows, the static test scripts fail to adapt the changes, resulting in test failures, flaky/brittle tests, or build failures. However, with AI & ML-based test automation , changes to an element locator (ID) are identified automatically and fixes to test scripts happen with self-healing capabilities, reducing the noise related to test failures.
Test Data Generation – Appropriate and proper data is necessary for robust testing. However, enterprises often struggle with transactional data since it is sensitive in nature. Manually synthesizing this data is not only time-consuming but also error-prone. However, with AI, data can be generated easily and can be used to test different scenarios.
More Reliable Test Cases – AI-driven test automation tools can understand your application by identifying relationships between document object model, and changes that occur to apps throughout time. Based on these changes, AI engines can autonomously generate test cases based on risks, eliminating the risk of business disruption with an application update. For instance, Oracle offers quarterly updates. The AI-based approach can be used to highlight the impact of the changes happen due to updates in Oracle Cloud testing. It helps in providing adequate test coverage while limiting the scope of testing based on highlighted impact.
Conclusion
In the age of Agile & DevOps, AI will help enterprises with more robust testing. Understanding the impact of the changes on the business processes, autonomous healing of the test scripts, and effortless test data synthesis can help in taking test automation to the next level. As an IT leader, you should be open to embrace innovation with AI to unleash the true potential of digital transformation
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