DeviQA report finds AI-assisted development is shifting more work to QA

DeviQA’s 2026 QA report says AI coding tools are getting features into testing faster, but they are also increasing queue sizes, regression work, and retesting cycles. The survey of 4,000 QA professionals shows teams are spending more time clarifying changes and checking downstream impact before release.

Why it matters: - AI-assisted development is speeding up implementation, but the testing burden is shifting downstream. - The report suggests teams may not see faster delivery unless they improve handoffs, acceptance criteria, and pre-QA verification. - The findings point to a broader operational risk: faster coding can create more review, regression, and retesting work for QA teams.

What happened: - DeviQA released The Impact of AI-Assisted Development on Software Testing: 2026 QA Report on September 24, 2026. - The study is based on responses from 4,000 quality assurance professionals with recent experience testing AI-assisted software. - Sixty-five percent of respondents said new features now reach testing sooner. - Nearly half said defect fixes are happening faster. - At the same time, 64% said more features arrive for testing at once. - Fifty-five percent said their QA queue has grown. - Fifty-two percent reported more testing-fixing-retesting cycles.

The details: - Fifty-eight percent said they examine related functionality more carefully when testing AI-assisted changes. - Fifty-six percent said they increased exploratory testing. - Fifty-six percent also said they increased end-to-end testing. - Fifty-two percent said they perform additional regression testing. - Forty-seven percent said AI-assisted functionality has sometimes passed its main scenario while causing problems elsewhere in the product. - Fifty-six percent said they spend more time clarifying expected behavior. - Fifty-two percent said they spend more time analyzing how a change affects other parts of the product. - Forty-four percent said dependencies between components were not adequately considered. - Forty-two percent said changes affected more components than expected or that the possible system impact had not been documented. - One Manual QA Engineer with five to seven years of experience said, “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.” - Clear acceptance criteria ranked first among risk-reducing practices, selected by 77% of respondents. - Other leading practices included discussing possible risks with developers, involving QA earlier, documenting impact on related functionality, splitting work into smaller changes, and requiring code review. - The report proposes an AI-ready definition of done built around seven questions: what changed, what else could be affected, whether the implementation matches acceptance criteria, what the developer tested, which automated tests were added or updated, what remains uncertain, and who reviewed the result. - The report says organizations should measure AI’s effect across the full delivery process, not just implementation time or code volume. - 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.

Between the lines: - The report frames AI coding tools as a productivity gain that can hide added verification work. - The data also suggests the biggest constraint is not code generation itself, but incomplete information and risk review before QA takes over. - The report cautions that the findings reflect reported experience, not controlled comparisons between AI-assisted and fully human-written changes.

What's next: - Teams are likely to focus more on acceptance criteria, earlier QA involvement, and tighter documentation around dependencies and downstream impact. - DeviQA says organizations should track QA workload and release-readiness metrics to see whether AI-assisted development is actually improving delivery. - The report argues that more verification needs to happen before the handoff to QA, not after it.