Survey of 4,000 QA professionals finds that features reach testing faster, while regression scope, testing queues, and retesting cycles continue to grow.

NEW YORK, NY, UNITED STATES, September 24, 2026 /EINPresswire.com/ -- DeviQA has released The Impact of AI-Assisted Development on Software Testing: 2026 QA Report, a new study examining how AI coding tools are changing the speed, scope, and workload of software testing.

Based on responses from 4,000 quality assurance professionals with recent experience testing AI-assisted software, the report finds that faster development does not automatically result in faster delivery.

Sixty-five percent of respondents said new features now reach testing sooner, and almost half reported faster defect fixes. At the same time, 64% said more features arrive for testing simultaneously, 55% have seen their QA queue grow, and 52% reported an increase in testing–fixing–retesting cycles.

The findings suggest that AI-assisted development is accelerating implementation while shifting part of the work downstream. QA professionals are spending more time determining what changed, clarifying expected behavior, evaluating the impact on related components, and extending regression coverage.

Among the report’s key findings:
– 58% examine related functionality more carefully when testing AI-assisted changes.
– 56% have increased exploratory testing, and the same proportion have increased end-to-end testing.
– 52% perform additional regression testing.
– 47% have encountered AI-assisted functionality that completed its primary scenario but caused problems elsewhere in the product at least sometimes.
– 56% spend additional testing time clarifying expected behavior.
– 52% spend more time analyzing the impact of a change on other parts of the product.

The reported challenges are not limited to visible defects. Respondents frequently pointed to missing or incomplete information at the development-to-QA handoff. Forty-four percent said dependencies between components were not adequately considered, while 42% reported that changes affected more components than expected or that the possible system impact had not been documented.

“AI writes code quickly, but a person is still responsible for it. The primary scenario may work while the business logic, edge cases, and related functionality break,” said one Manual QA Engineer with five to seven years of experience who participated in the study.

The research also identifies the practices that help teams manage these risks. Clear acceptance criteria ranked first, selected by 77% of respondents. Other leading practices included discussing possible risks with developers, involving QA earlier, documenting the impact on related functionality, dividing work into smaller changes, and requiring code review.

The report proposes an AI-ready definition of done built around seven questions teams should answer before handing functionality to QA:
– What changed?
– What else could be affected?
– Does the implementation match the acceptance criteria?
– What did the developer test?
– Which automated tests were added or updated?
– What remains uncertain?
– Who reviewed the result?

The report concludes that organizations should measure AI’s effect across the full delivery process—not only through implementation time, code volume, or completed development tasks. Relevant indicators include the time from development completion to release readiness, QA queue size, regression scope, repeated testing cycles, and defects found outside the directly modified functionality.

“The strongest message in the data is not simply that teams need to test more. It is that more verification needs to be completed before the handoff to QA,” the report states.

About the research

The online study was conducted from August 15 to September 15, 2026. It included 4,000 Manual QA Engineers, Automation QA Engineers, SDETs, QA and Test Leads, and other quality engineering professionals.

Every participant confirmed that they had personally tested functionality developed with AI coding assistants during the preceding six months. Respondents were recruited through DeviQA’s professional community, and participation was voluntary and not incentivized.

The findings reflect participants’ reported experiences rather than controlled comparisons between equivalent AI-assisted and fully human-written changes. The report therefore identifies where QA effort is shifting without claiming that AI-assisted development is the sole cause.

About DeviQA

DeviQA is a global software quality engineering company founded in 2010. Its team of 300+ QA engineers has delivered more than 600,000 person-days of testing across 500+ projects for 300+ clients in over 40 industries. Clients include Abbott, BP, Tipalti, Mimecast, Compass, and other product companies ranging from startups to global enterprises.

DeviQA provides end-to-end quality engineering, test automation, manual testing, performance and security testing, AI system testing, managed QA services, and dedicated QA teams. The company maintains a professional community of more than 4,000 QA specialists, a testing lab with 300+ real devices, and a 96% engineer retention rate.

DeviQA is certified to ISO 9001, ISO/IEC 27001, and ISO/IEC 20000-1 standards. The company was named a G2 Leader in the Testing and QA Providers category, ranked first among the Best QA Outsourcing Companies by SuperbCompanies, and included in the Clutch 1000.

Oleg Sadikov
Flyant SL
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