For anyone who is looking to improve the efficiency of their testing team, a shift to automation testing can prove to be a lifesaver. Manual testing is no longer able to meet the constant changing demands of the business landscape today. With automation, testing teams can run flawless test cases and get updated results within minutes.
Whenever a new functionality is added into the application, testers can write the automation scripts to pool the automation suite after manually verifying those changes.
Sometimes because of the test pool density, the system is not competent enough to run the required test case on its own. This results in the tests being queued up, and thus the testing teams fail to analyze the results/build. In this blog, we will explore how AI has helped testing teams cope with overloads and improve automated testing efficiency.
Development of Test Cases:
- Test cases are developed, modified, and run in a smart way rather than executing an extensive test suite to verify minor change/ bug-it helps us in achieving only the related test cases.
- AI enables the team to analyze more possibilities for the business flow. It efficiently generates the least repeating data, thus, helping find the new approaches to test cases.
- Test cases are intelligently diagnosed by AI to diagnose the impact of the code update.
Execution of Selenium Tests:
A few cons of Selenium-based automated test cases include complexity, lengthy execution periods, and requirement dependency.
AI classifies broken test cases and fixes them. It determines the cause of failure for the test cases, rebuilds them, and then suggests essential improvements.
API Tests
API testing assures that the communication between different programs through connection/network is stable. This ensures that the requests are processing fine, enabling end-users to get valid results.
During the application development, some services consume many APIs, hence forcing the way for automation testing.
AI evaluates the functionality of linked applications, reveals risky areas, and helps build test cases that penetrate through those areas. The capability of studying larger data sets helps to develop precise test cases. AI also tests APIs to make sure that they are performing correctly.
Predictive Analysis
The analysis of usage patterns by application users and data analytics AI predicts how browsing behaviors evolve. It helps the team to be a few steps ahead of the continually changing users’ standards. By identifying and removing these future hurdles, predictive analysis allows for improved service quality. AI, in partnership with Machine Learning, forecasts better user behavior and serve with more precision.
UI Testing
UI Automation Testing is the use of automation for the testing of visual elements, their position, color, and size.
UI automation testing has become quite a challenge for testers as there has been a massive increase in the technologies that require UI testing. From android to IOS, windows to mac, and other smart devices alongside, there is a need to visualize the users’ perception about layouts, aspect ratios, and then writing the cases accordingly. This is a daunting task that the manual tester is not equipped to do efficiently. UI testing, however, can make this job easily by defining the scope.
Since it is impossible for the tester to execute all scenarios, the best approach is to use input files for UI automation testing. The application’s environment is analyzed by AI along with related parameters like firmware, OS, browsers, and aspect ratios. Once the analysis is complete, it then adjusts the output/ comparison parameters accordingly to generate accurate user-oriented results.
End Note:
AI has become a market reality. Many other areas benefit from it by integrating it with different domains in computer science. AI unlocks countless prospects for automation testing as well. With its involvement, scripts are now more accurate, and suites are more intelligent in terms of execution. The self-healing capacity generated through continuous learning decreases the chances of script failure or false results. It helps us in simplifying UI/ Visual Tests, alongside extensive data analytics related tests. Conclusively, AI helps in identifying the future customer trends and mend the application functionality accordingly. It is a game changer for all those invested in testing.
I hope this blog has helped you understand the increasingly significant role of AI in the world of automation testing. Stay tuned for our next blog in which we will cover the following:
- How can we train the models and use open-source models?
- How can A.I testing expedite the overall process?
- Manual testing vs. autonomous testing.
- How can we generate test data using AI models?
If you have any questions or comments, do not hesitate to reach out to us.
Happy testing!
