AI coding assistants have made developers far quicker. Features that took a sprint to build now take days. Testing teams have felt the effect of that speed first.
A new study from DeviQA puts numbers on what testers are seeing. It surveyed 4,000 QA professionals between August and September 2026. Each one had tested AI-assisted software in the past six months. The core finding: code moves faster while testing work keeps piling up.
This blog looks at where QA teams are struggling most. Under each pain point, we show how our QualNetics.AI platform solves it.
Key Takeaways
- AI-assisted coding sends more work to QA, and queues are growing.
- The hardest testing problems start with missing context about each change.
- More AI-written tests also means more tests to maintain.
- AI test automation must cover legacy desktop apps as well as web.
- Governance and model choice now decide whether AI testing can scale.
What Is AI Test Automation?
AI test automation uses AI agents to plan, write and run tests. The same agents keep those tests working as the application changes. Older automation relied on engineers writing and fixing each script by hand. AI test automation takes much of that manual effort away.
How Is AI-Assisted Development Changing Software Testing?
The speed gains from AI coding are real. In the survey, 65% said new features reach testing faster. Almost half said defects were also getting fixed faster.
The pressure has moved downstream, though. 64% said more features now land in testing at the same time. 55% have watched their QA queue grow. 52% reported more rounds of testing, fixing and retesting.
An earlier study of 300 testers found a similar strain. 58% said their workload had grown, and 52% saw more bugs.
The research frames this as a delivery problem for the whole team. Development finishes sooner, but release readiness often does not move. The report notes its data is self-reported, with no control group. Even so, the direction is hard to ignore.
The World Quality Report 2025-26 shows a related gap inside QA itself. About 89% of organizations are piloting or using GenAI in quality engineering. Only 15% have scaled it across the enterprise.
What Is QualNetics.AI?
QualNetics.AI is our AI quality engineering platform for enterprise teams. We built it at PrimeSoft to take on the slowest QA work. Its AI agents generate, run and maintain tests on their own. They work across desktop, web, mobile and API layers. That includes legacy and thick-client desktop apps that many tools skip.
We designed QualNetics.AI around three principles.
- Autonomy means the agents handle repetitive testing work.
- Governance means every AI action stays visible and auditable.
- Enterprise fit means it works with the tools you already run.
How Does QualNetics.AI Work?
We get QualNetics.AI running on your codebase in five steps.
- Assess and Discover: We map your desktop, web, API, mobile and ERP apps within weeks.
- Connect and Configure: You plug in your preferred AI model and existing toolchain.
- Generate Test Assets: Our agents produce unit, API, UI, end-to-end and desktop tests.
- Execute and Self-Heal: Tests run continuously, and broken ones repair themselves.
- Analyze and Improve: Dashboards show failures, coverage gaps and performance trends.
No infrastructure overhaul is needed at any step. With that context, here is how it maps to each pain point.
What Are the Biggest Pain Points in AI Test Automation, and How Do We Solve Them?
We built QualNetics.AI, our AI quality engineering platform, for enterprise QA teams. Our agents generate, run and maintain tests across desktop, web and mobile. Below, each pain point sits right next to how we address it.
1. Testers Do Not Know What Changed
The problem: Many testing problems start before a single test runs. 44% of testers said dependencies between components were not considered. 42% said changes touched more components than expected. Only 18% said the information they received was usually enough. So testers spend hours rebuilding the story of each change. In the survey, 52% spend extra time analyzing impact on other areas.
How QualNetics.AI solves it: Our agents build test plans from your requirements and code. QualNetics.AI connects to Jira and Confluence, where acceptance criteria usually live. The agents then produce test cases, test data and automation scripts. Coverage spans unit, API, UI, desktop and end-to-end layers. We also prioritize test cases by functionality changes and feature impact. Your testers start from a plan grounded in the actual change.
2. The Main Flow Works While Other Parts Break
The problem: 47% have seen AI-built features pass the main scenario yet break elsewhere. The most common defect was disruption of existing functionality, at 44%. Related modules were affected for 42% of respondents. Legacy components were affected for 23%. That is why 52% now run extra regression tests.
How QualNetics.AI solves it: QualNetics.AI runs regression continuously across desktop, web and mobile. Our API testing covers parameter variations, error handling and security checks. Those API tests update on their own as endpoints change. Performance and security testing run within the same cycle. Your team catches side effects before a fix returns for retesting.
3. Test Maintenance Grows With Every Release
The problem: AI makes it cheap to create large numbers of tests. Each new test is one more thing to keep passing. UI changes break locators, and flaky tests erode trust in results. When failures become noise, teams stop trusting their own suites.
How QualNetics.AI solves it: When an app change breaks a test, QualNetics.AI repairs it. It updates locators, parameters and test flows without manual effort. Your engineers spend time on new coverage instead of fixing scripts. Fewer broken tests also mean fewer false alarms in the pipeline.
4. Legacy and Virtualized Desktops Are Hard to Automate
The problem: Many enterprises still run thick-client and legacy desktop applications. These often expose no DOM or object tree to automation tools. Apps delivered through Citrix or VDI often arrive as image streams. Selector-based tools struggle there, so testing falls back to manual work.
How QualNetics.AI solves it: QualNetics.AI needs no DOM, API or source code access to test. It uses object recognition when an object tree is available. When there is none, it switches to on-screen visual recognition. This covers native, legacy, thick-client and virtualized desktop applications. You can automate apps your team once had to test by hand.
5. Teams Do Not Fully Trust AI Output
The problem: The World Quality Report names the main barriers to scaling AI. Data privacy risk was cited by 67% of respondents. Integration complexity followed at 64%. Hallucination and reliability concerns came in at 60%. Half of organizations also said they lack AI and ML skills.
How QualNetics.AI solves it: We designed QualNetics.AI so you stay in control of models and data. You choose OpenAI, Gemini, Anthropic Claude or on-premises Llama. We deploy it in the cloud, on-premises or in a hybrid setup. Regulated teams can keep sensitive data inside their own environment. SAML SSO and full AI interaction audit trails are built in. LLM budget management keeps model spend visible and under control.
QualNetics.AI also plugs into Selenium, Cypress, Appium, TestRail and Zephyr. It works with GitHub, Bitbucket and Azure DevOps too. That cuts the integration effort the World Quality Report flags.
What Results Can Teams Expect From QualNetics.AI?
With QualNetics.AI, we deliver up to 60% faster QA cycles. We also deliver up to 40% fewer production defects. We see 2x team productivity and up to 80% higher automation ROI.
Every rollout starts with an Assess and Discover step. We map your desktop, web, API, mobile and ERP apps within weeks. You then connect your preferred AI model and existing tools. No infrastructure overhaul is needed.
What Should QA Leaders Do Next?
The survey respondents named clear acceptance criteria as the most helpful practice. 77% chose it, far ahead of stable automated tests at 23%. That point matters for anyone buying AI testing tools.
AI test automation works best on top of strong QA basics. Clear requirements, small changes and a named human reviewer still count. We built QualNetics.AI to carry the repetitive load around those basics. Your people keep the judgment calls.
Frequently Asked Questions
What is AI test automation?
It uses AI agents to generate, run and maintain software tests. The agents adapt the tests as the application changes.
Why does AI-assisted coding increase QA work?
More features reach testing at once, often with unclear impact. Testers must then check related areas and rerun regression suites.
What are self-healing tests?
Self-healing tests repair themselves when the application changes. The tool updates broken locators, parameters or steps automatically.
Can AI test legacy desktop apps without source code?
Yes. Our QualNetics.AI platform uses on-screen visual recognition for this. It works even when no DOM or object tree exists.
Is AI replacing QA engineers?
No. AI takes on repetitive work like scripting and maintenance. People still own test strategy, risk calls and final review.
How should teams measure AI's impact on testing?
Track QA queue size, retesting cycles and regression scope. Also track defects found outside the code that changed.
See QualNetics.AI on Your Codebase
The best test is your own application. We can start with a proof of concept on your codebase. Request a QualNetics.AI demo.