Importance of AI in Test Automation

Posted By :Nidhi Ojha |24th January 2021

Reducing Maintenance and Eliminating Flaky Tests:
 

One of the most frequent issues with test automation is maintenance. For instance, say we have 100 automated tests running daily to ensure the main functionalities of the application are still stable; What if the next day we come back to work and notice that half of the tests have failed? We would require to spend considerable amounts of time to troubleshoot the failures and investigate what happened. This implies figuring out ways to fix the failures and implement the fixes. Then, we re-run the automated tests to make sure everything passes.

                                            

AI can avoid bugs like this due to its self-healing mechanism. It can begin detecting problems in the tests before they even occur, thus proactively fixing tests instead of us reacting to them. Based on the number of iterations the tests have run, the AI can figure out which tests are stable or skaly. As a consequence, it can give us data on what tests need to be modified to make sure test runs are stable.

 

Based on great numbers of test runs, AI can optimize the wait times used in tests to wait for the pages to load and also can handle tests running on different resolutions.This solves one of the major bottlenecks of continuous testing as this adds up to a considerable decrease in the time spent on the maintenance of tests.     

 

Automatically Writing Test Cases:
 

The huge application of ML/AI in test automation has been in automatically writing test cases for software. AI/ML tools have gone far ahead to grasp the business usage scenarios of the application under test. They just need to be cuspated to the software. While learning the application, they automatically crawl and gather useful data like screenshots, HTML pages, and page loading time. Over time they gather enough data from the application so that they can train the ML model for expected patterns of the app.

 

When they are run/ executed, the current state of the app is differentiated with the known or saved patterns. If there is any issue, visual difference, slow run time, or similar issue, then the system automatically marks it as a solid issue. But in some cases, the differences might be valid. In that case, the tester requires to validate the bug or issue.

 

Removing Dependencies:
 

Another provocation of test automation is writing tests for an application that may have dependencies on other modules that may or may not have been implemented yet. Now AI can help out to do this for us.

 

Once we have drafted some tests and have run them consistently for some time, the AI can begin recording all the server responses. The next time we run the tests, instead of talking to a server or database, the test will access the stored results (which was facilitated with the help of AI) and will endure running without any obstacles.

 

Conclusion:

As a result, the tests run much faster, since the delay in waiting for a response is removed and the need to depend on a physical database or server has completely been erased.
 


About Author

Nidhi Ojha

Nidhi is certified in Manual Testing and SQL. She has done B.tech in Electronics and Communication branch, apart from this she loves to travel to different places.

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