Artificial Intelligence for Testing
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts. Separated they live in Bookmarksgrove right at the coast of the Semantics, a large language ocean. A small river named Duden flows by their place and supplies it with the necessary regelialia. It is a paradisematic country, in which roasted parts of sentences fly into your mouth.
The Big Oxmox advised her not to do so, because there were thousands of bad Commas, wild Question Marks and devious Semikoli, but the Little Blind Text didn’t listen. She packed her seven versalia, put her initial into the belt and made herself on the way.
Although applying the artificial intelligence and machine learning (AI/ML) approaches to address the challenges in software testing is not new in the software research community, the recent advancements in AI/ML with a large amount of data available pose new opportunities to apply AI/ML in testing.
However, the application of AI/ML in testing is still in the early stages. Organizations will find ways to optimize their testing practices in AI/ML.
AI/ML algorithms are developed to generate better test cases, test scripts, test data, and reports. Predictive models would help to make decisions about where, what, and when to test. Smart analytics and visualization support the teams to detect faults, to understand test coverage, areas of high risk, etc.
We hope to see more applications of AI/ML in addressing problems such as quality prediction, test case prioritization, fault classification and assignment in the upcoming years.