Bringing AI To Test Automation
Considering the increasing complexity of the software and contraction of a release cycle, the tester is much stressed to provide the quality feedback instantaneously. In the era of continuous testing, testing smarter, not harder mantra is being adopted by many organizations as no other choice is left for such a rapid software launches. Nowadays, not only releases are squeezed from monthly to weekly but also updates are done several times a day.
The only way to make software testing smarter and more efficient is by bringing Artificial Intelligence(AI) in this domain. The incorporation of machines that can emulate human behavior can not only enable us to move beyond the manual testing process but also, we would be able to move towards precision-based automated continuous testing.
For more than a year, Tricentis has been uniting AI deep learning and continuous testing. According to a statement given by Gerd Weishaar, Chief Product Officer at Tricentis, changed controls have been optically recognized by the continuous testing platform with more accurate results than human and ever-improving algorithms for capturing the slightest change.
AI is also being used in object application categorization for each User Interfaces within test automation. When you create tools, recognized controls are categorized. For out-of-the-box set-up, users can also pre-train commonly seen controls. Further algorithms are required for Optical Character Recognition(OCR). You can create a technical map from the hierarchy of controls and so the AI is looking at the GUI to get the label for the control.
It takes a while to see the positive results with learning algorithms, said Gerd. The core transactions can be distilled by a human user and machine learning from the business using deep learning. Tricentis is using this technique with a partner in information systems companies for SAP monitoring and testing to note details on the user side, log cases, and create custom-made cases without human interaction.
Verification of results is done by testing only. You need to access test data. Google DeepMind invented an AI program using deep reinforcement learning for playing video games by itself, hence generating loads of test data.
User preference can be monitored and categorized, a risk preference can be assigned accordingly. This data is a classical case for automated testing to learn and find out divergence. Bottlenecks can be identified through Heatmaps. Heat maps can also help to discern which tests you need to perform. We can’t rely on classical data analysis as there is some flakiness. Testers should look for redundant test cases and convert manual test cases in automating it so as they can be more focused on making data-driven decisions.
As Automation Testing goes to the cloud, one user is learning from other users while keeping their data secure and apart. However, taking a dive into the core, we understand that repository tests are highly connected with one another, providing a data point for every relationship. AI neural networks are getting stronger as more data is being added.
Risk-based test automation helps users find which tests to run for most coverage when given a limited amount of testing time. By bringing AI to test creation, execution, and data analysis, testers can more quickly identify controls, spot links between defects and other components, and eliminate the need to constantly update test cases manually.