Most brittle suites fail on harmless UI movement. Zerocheck uses semantic resolution, accessibility data, cached selectors, and confidence checks, then fails with evidence when it cannot act confidently.
“The same pattern kept repeating itself: a harmless-looking UI change, a merge, and then a failing test in CI.”
monday.com Engineeringsource
“We completely lost trust in our build, and red builds no longer meant anything.”
ThoughtWorks Engineeringsource
46% of developers distrust AI testing accuracy
30% of GenAI projects abandoned after POC due to trust issues
Testim users report 'tests do not heal themselves under any circumstance'
Selector-based healing (Mabl, Testim, Katalon) guesses alternative selectors when the original breaks. The guess can latch onto the wrong element, creating false passes that mask real bugs.
Intent-based tools (Momentic, testRigor) are more resilient but opaque. Engineers describe them as a 'black box.' When the AI adapts, nobody can explain why.
The gap: AI-assisted execution should expose enough evidence for engineers to trust failures and passes. Low-confidence behavior should fail with context, not silently pass.
UI redesign ships. Mabl 'heals' 12 tests by finding alternative selectors. 10 heal correctly. 2 latch onto the wrong element and now validate a different flow entirely. Tests pass. Bug ships to production. Team discovers it from a customer report 3 days later.
Same redesign. Zerocheck uses semantic resolution, accessibility data, cached selectors, and confidence checks. High-confidence interactions continue; low-confidence interactions fail with screenshots and step traces instead of silently passing.
Tests describe user intent in plain English, not selectors
Visual interaction layer navigates like a human user
Low-confidence interactions fail with screenshots and step traces
Low-confidence changes fail-closed instead of silently passing
Other tools prove their own platform is secure. Zerocheck produces JSON evidence from your executed application tests.
Get coverage on the flows customers will notice when they break, without turning testing into a quarter-long infrastructure project.
Guard the only code path where a bug is measured in lost dollars per minute.
Resilience engineers can inspect. Low-confidence actions fail with evidence instead of silently passing.
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