Transparent Self-Healing

    Self-healing that shows its work

    Most AI testing tools heal silently. You find out after merge that the test now validates the wrong thing. Zerocheck shows every adaptation with confidence scores so you approve changes before they ship.

    Who this is for

    Role
    Senior engineer or QA lead
    Company
    Teams evaluating or already using AI testing tools (Mabl, Testim, Momentic)
    Trigger
    AI tool healed a test that silently masked a real bug. Or: team disabled self-healing because they couldn't trust it.

    The pain is real

    “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'

    Why nobody else solves this

    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.

    Nobody combines intent-based resilience with transparent, auditable adaptation. The gap: AI should act, but it must produce a deterministic explanation trail.

    The workflow today vs. with Zerocheck

    Without Zerocheck

    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.

    With Zerocheck

    Same redesign. Zerocheck detects the changes via visual interaction. Generates adaptation report: 'Button moved from sidebar to header. Confidence: 97%. Interaction path updated.' Engineer reviews, approves. 2 low-confidence adaptations flagged: 'Confidence: 62%. Recommended: manual review.' Tests fail-closed on low confidence instead of silently passing.

    How it works

    1

    Tests describe user intent in plain English, not selectors

    2

    Visual interaction layer navigates like a human user

    3

    Every adaptation generates a human-readable change report

    4

    Low-confidence changes fail-closed instead of silently passing

    Self-healing that shows its work

    Self-healing you can actually trust. Every adaptation is visible, reviewable, and auditable.

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