There is no doubt about it: artificial intelligence is here to stay and changing the quality assurance landscape at unprecedented speed.
The tech has become a catalyst in facilitating the use of automation to complete tasks. It has also allowed for deeper levels of analysis of data and can report its results in a format that can be readily consumed by users without a technical background.
Furthermore, it allows for users to make quick and informed decisions based on vast amounts of data, according to Margarita Simonova, the founder and CEO of ILoveMyQA.com.
“Its effects have also made a strong impact on the field of quality assurance (QA),” Simonova said.
“These changes come with immense benefits, but also introduce new obstacles that must be addressed,” she added.
Benefits
Simonova was keen to highlight, first of all, the immense benefits AI has brought to software testing, with automation being the most obvious one.
“With AI, developers no longer need to manually run their QA routines or do regression, functional and load testing—these chores can now be fully automated,” she wrote in a recent Forbes analysis.
Moreover, predictive analytics has received a major boost from AI.
“In a traditional environment, errors are fixed after they have been made. Now, AI algorithms can proactively prevent errors from occurring by analyzing code and making adjustments before errors even happen. Because of this, the role of a QA tester has shifted more into data analysis.”
“The role of a QA tester has shifted more into data analysis.”
– Margarita Simonova
Also think of exploratory testing, Simonova continued.
“In this type of testing, the tester does not follow a predetermined path, but is able to explore the application freely,” she explained.
“AI is now able to handle this task and, in doing so, is able to find new bugs and issues with code that previously would not have been found without spending countless hours of manual testing.”
Obstacles and challenges
Despite the impressive technological advance, the presence “of AI has not always been positive,” Simonova said.
“AI does not just casually learn how to perform its various functions, it requires a vast amount of carefully selected data to be fed to it. All of this data has to be curated by QA professionals,” she stressed.
“They must ensure that the data being fed to the system is accurate, otherwise the AI model will be trained incorrectly.”
Moreover, think of “mysterious methods,” as Simonova put it.
“In general, many people still do not fully understand how AI works. Even AI experts don’t always understand how the system is learning,” she noted.
“On top of that, the companies that make AI often try to keep their methods secret. Not fully understanding how AI works can become a big issue.”
Finally, AI is still rapidly evolving.
“To keep up, a QA professional needs to be constantly on their toes, continually learning about the latest trends,” Simonova highlighted.
“This includes learning about the latest AI models and how they have changed from their predecessors,” she continued.
“This means that despite being a tool to help QA professionals more efficiently do their jobs, AI also takes a lot of work to master and keep learning about,” Simonova concluded.
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Test automation, data and software risk management in the era of AI
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