In the age of widespread application of AI for enhanced convenience and intelligence, AI-driven automation testing is no exception. As we stand on the brink of realizing AI-powered self-driving cars, it is only logical to leverage AI in automation testing, particularly considering that 80% of testing tasks are repetitive and amenable to automation. The evolution of automated testing, or testing in general, must cater to the demands of today’s intricate requirements in a faster and more efficient manner.
The imperative is to automate a target of 80% of repetitive testing tasks, leaving the remaining 20% for manual effort. As development teams increasingly adopt Continuous Testing, the techniques employed in test automation must grow smarter to ensure greater effectiveness in the delivery pipeline.
There exists ample potential for AI to significantly enhance the capabilities of test automation.
Below are several key ways to leverage AI in test automation:
Test Runs and Executions
Creating test runs encompassing all relevant and impacted test cases to address business risks for each application change is an ongoing and repetitive task requiring significant effort. It is crucial to ensure that existing features function as expected during the development and testing of new features. However, creating test runs incorporating new and old tests to cover all business risks can be laborious. Moreover, Only some people possess comprehensive knowledge of an application to identify the affected test cases accurately. Manually reviewing each test case within short delivery cycles is near impossible, and many test cases are simply omitted.
The easy summation is, testing everything in every iteration is not cost-effective absent the power of machines. Furthermore, as the pressure to release software applications increase, more effective test runs must be created more rapidly. Achieving 100% effectiveness with human intervention is not possible. Therefore, the adoption of ML and AI driven testing is critical. Machines can automatically identify in a specific format what has been tested and what is yet to be tested. This helps us understand test coverage over successive runs, but more importantly, application coverage run over run. With the aid of machines, we can now spot trends and see analytical views in real-time. AI writes scripts autonomously at the speed of a machine, ensuring continuous application quality monitoring.
Test Case Maintenance
Maintenance is a significant and ongoing effort in Test Automation. Whenever an application changes, it becomes necessary to identify the affected automated test cases and modify them accordingly. A dynamic locator strategy can be employed instead of relying on static locators to determine elements in the user interface (UI). This enhances the robustness and reliability of tests while reducing maintenance efforts.
AI identifies all tests directly or indirectly associated with application changes. It also allows for automated modification of affected tests, simplifying the overall maintenance process.
Learning from Test Failures
When a test that previously passed fails, testers typically spend time identifying the root cause before fixing it. Suppose the failure is due to a system-level change or an alteration in application behaviour. In that case, the team may search for other affected tests instead of solely addressing the problem. This consumes unnecessary time and manual effort.
Even if all tests are executed in such cases, manually debugging each failed test one by one and filtering similar failures remains necessary. In such scenarios, machines can identify similar failures in other tests without executing all of them, eliminating the need for individual debugging.
Whereas by analyzing available information, AI in Test Automation can identify and fix similar tests affected by a failed test. It allows for examining how a failed test has performed over time, determining the cause of the failure. This information is then utilized to identify other potentially affected tests.
Additionally, AI utilizes test result history to quickly generate test runs for upcoming releases, facilitating more stable and efficient releases.
Test development is an area within test automation that requires substantial initial effort, time, and highly skilled resources. AI offers potential solutions to reduce these efforts:
Creating automated tests or test scripts directly from requirement documents using Natural Language Processing techniques.
Autonomous navigation through all pages of a UI-based application to automatically create tests that cover various scenarios.
Training Test Bots to perform specific actions with input data, employing AI to enhance the process and outcomes.
Visual recognition for GUI applications, such as Google’s image recognition, detects changes in a page based on graphical images.
These represent potential areas to initiate the implementation of AI in Test Automation. Over time, AI will be capable of autonomously creating, executing, and maintaining automated tests with minimal human supervision rather than requiring direct human intervention.
We acknowledge that traditional automation testing tools and practices are no longer sufficient in today’s landscape, where Agile and DevOps development practices are prevalent. There is a significant opportunity for AI in Test Automation to provide improved automation testing. We require more advanced and intelligent approaches that address directly impacted tests and reduce overall maintenance efforts. This is essential for enhancing the reliability and stability of automated testing.
In the future, we anticipate more significant adoption of AI to tackle major challenges in Test Automation, resulting in faster, more effective, and cost-efficient Automated Testing for the next generation, similar to how QAaaS aims to revolutionize the industry from the beginning.