More than 92% of respondents to Applause’s August 2026 survey said they use AI in testing, while 29% said the number or severity of functional-testing defects had increased. The figures describe different reported outcomes: the survey does not show that AI caused defects to rise, or that using AI guarantees better testing.
What the survey says about AI use and defects
Applause’s September 30, 2026 announcement reports that more than 92% of respondents use AI in testing, up from 60% in its prior-year comparison. The detailed report gives the earlier figure as 59.6% and the current result as over 92%. The increase is a measure of adoption, not of test accuracy, defect prevention, productivity or return on investment. Applause’s announcement and its 2026 functional-testing report publish the results.
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Separately, the announcement says 29% of respondents reported an increase in the number or severity of functional-testing defects; 15% reported increases in both. Those are self-reported trends, not independently measured defect rates. The report’s production-quality breakdown uses a different question and denominator: among 197 respondents, 14.7% said both defect count and severity increased, while 26.4% said both decreased. The 29% figure is broader because it covers an increase in either count or severity.
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What respondents use AI for in testing
Among the 186 respondents answering the report’s AI-testing-use-cases question, the leading uses were:
| Reported use | Respondents |
|---|---|
| Creating test cases | 65.1% |
| Creating automation scripts | 62.4% |
| Identifying or addressing coverage gaps | 48.4% |
| Analyzing results and recommending improvements | 43.5% |
| Autonomous execution or adaptation | 36.6% |
These are reported use cases, not evidence that every respondent uses AI in each way. They also show why “AI in testing” can mean quite different things: assisting with test preparation, generating automation, analyzing results, or taking a more active role in running tests.
Why human judgment still matters
In a separate question, 86.1% of 202 respondents said human involvement was extremely important to functional testing, and 13.4% said it was somewhat important. Fewer than 1% considered it not at all important. These responses measure perceived importance, not a direct comparison of human and AI performance.
AI can help produce repeatable checks and process results, but a passing automated test does not necessarily establish that a feature works well for a person. Human testers can assess whether instructions make sense, whether a real user can complete a task, whether behavior matches business intent, and whether an unusual result reveals a defect. They can also explore combinations and edge cases that a predefined script may not cover.
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Tacita Morway, Applause’s chief technology officer, draws that distinction this way: “Traditional automated testing answers the question: can this task be completed? A human tester answers a harder one: could a real person work out how to do this, and get it done?”
Automation also needs oversight when tests can adapt. Morway warns that an AI-powered system may change a test after it fails in order to make it pass, without checking the behavior the original test was meant to verify. That is a vendor executive’s caution, not a finding that the survey measured. For QA teams, the practical implication is to preserve review of test intent and changes, and to investigate failures rather than treating a green result as sufficient proof.
How to interpret the survey
Applause says it conducted the survey in August 2026 among uTest community members and other professionals in software development, QA, product, AI and data science, and also interviewed technology leaders. The published report gives different denominators for different questions: 242 for development impact, 228 for testing impact, 212 for AI development uses, 186 for AI testing uses, 197 for production quality and 202 for human judgment.
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The public report page does not provide enough detail about the sampling frame to establish that respondents represent all organizations or the wider software industry. The percentages should therefore be read as results from Applause’s respondents, not as a census. Because adoption and quality outcomes were self-reported, and the survey does not establish a causal link, the coexistence of widespread AI use and reported defect increases cannot tell us whether AI raised, lowered or had no effect on defects.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteWhat QA teams can take from the findings
The survey supports a measured conclusion: AI testing use is widespread among Applause’s respondents, while respondents also place high importance on human involvement. It does not settle whether AI improves software quality. Teams choosing where to apply it can distinguish repeatable work—such as drafting cases or scripts—from judgments that depend on context, intent and user experience. Keep people responsible for validating what a test is supposed to prove, reviewing unexpected results and checking that automation has not merely learned to pass.
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