As artificial intelligence-powered testing solutions are being rolled out left, right and centre, with banks and other financial firms rushing to embed the technology, industry observers generally welcome the uptake of AI.
Judy Bossi, VP of Product at Idera, the company that runs QA test management platform TestRail, is no exception.
“Although it’s still early to gauge AI’s full impact on QA, we think the future looks bright,” said Atlanta-based Bossi.
“Like test automation before it, I am optimistic that AI will not only speed up release cycles and improve software quality but also empower testers to spend more time on tasks that require human insight and intuition,” she added.
Bossi singled out an AI-focused research survey carried out by her firm recently, which she labelled as “the QA industry’s first”, for which 1,000 QA professionals were interviewed.
The researchers found that a vast majority, namely two thirds, of QA professionals said they use AI in their QA processes, with AI being applied uniformly across various testing types. However, certain use cases show more potential than others.
“We aimed to cut through the hype surrounding artificial intelligence, offering a clear and accurate picture of how QA teams are adopting, planning for, and responding to AI technology,” she clarified.
Bossi said her firm focused on AI’s applications in areas such as regression testing, test automation, and performance testing, scrutinising how AI is helping teams increase efficiency, improve test coverage, and enhance overall quality.
Half of all respondents firmly believe AI increases productivity and improves test coverage. “This underscores AI’s potential to enhance testing efficiency,” Bossi said.
Then researchers looked at AI’s versatility. “AI is proving effective across various testing types, including regression, smoke, and performance testing,” Bossi continued.
“It is being used to generate test cases, execute tests, and analyze results, allowing human testers to focus on strategic tasks.”
Some barriers and obstacles remain, however.
Despite high adoption rates, challenges like AI’s complexity, concerns about data privacy and security, and uncertainty about its benefits continue to hinder broader implementation, Bossi said.
The most significant challenge reported is uncertainty about AI’s effectiveness, with more than half of all respondents expressing this concern.
Additionally, many QA professionals noted a need for more skilled personnel and highlighted the complexity of AI tools and technologies as obstacles to successful integration.
“These insights reveal the ongoing challenges that organisations face as they work to incorporate AI into their QA processes,” Bossi noted.
Finally, most QA professionals indicated AI will not replace human testers. “As long as software is made for people, the human element in testing will remain essential,” she concluded.
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