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Drizz raises $2.7M in seed funding •
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Featured on Forbes
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Drizz raises $2.7M in seed funding •
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Featured on Forbes
Mobile test automation breaks when the UI changes: Element IDs shift, layouts move, popups appear, and locator-based scripts fail.
Self-healing mobile test automation solves this by allowing tests to adapt to UI changes automatically.
Drizz replaces locator dependency with Vision AI and natural language execution, ensuring tests continue to run even as the interface evolves.
Self-healing mobile test automation is an approach where tests adapt to UI changes automatically instead of failing when selectors break. It identifies elements using visual context, text, and layout rather than relying only on static locators.
Drizz enables self-healing mobile test automation by interpreting each test step visually and contextually at runtime. The system reads the screen, understands intent, and completes the action using multiple signals:
When the UI changes, Drizz re-identifies the intended element instead of failing. This removes the primary source of flaky mobile tests.
Drizz uses Vision AI to enable self-healing mobile test automation without relying on selectors.
Instead of targeting elements using IDs or XPath, the system:
This approach allows AI self-healing test automation to:
Vision-based execution is what enables self-healing mobile testing to work reliably in dynamic environments.
Flaky tests in mobile automation are typically caused by unstable selectors, timing issues from dynamic UI, and inconsistent environments. Self-healing test automation reduces flakiness by removing these root causes.
With Drizz:
As a result, teams see fewer false failures in CI, more reliable test results, and reduced debugging time.
Learn how to reduce flaky mobile UI tests in CI
Locator-based frameworks such as Appium rely on static identifiers like XPath, accessibility IDs, or CSS selectors. These break whenever the UI changes.
Common limitations of locator-based testing:
Self-healing mobile test automation removes this dependency entirely.
Instead of fixed selectors, Drizz:
This results in:
Traditional frameworks rely on element IDs, XPath or CSS selectors, and manual updates whenever the UI changes.
Drizz enables self-healing test automation by removing locator dependency.
Tests are written in plain English:
The platform converts these into executable steps using Vision AI. No selectors, no keys, no maintenance overhead.
NikahForever ran 50+ test cases with zero locator usage and maintained stability across frequent UI updates.
Learn more about Drizz's case studies.
Dynamic elements such as popups, delayed rendering, layout shifts, and identical components are the primary sources of flakiness.
Drizz handles these scenarios by adapting to UI changes in real time, identifying the correct element even when duplicates exist, and executing flows reliably across complex screens.
This allows tests to survive continuous product iteration without rework.
Learn more about dynamic UI testing using Vision AI.
Drizz includes an execution layer designed for reliability:
This reduces:
The system distinguishes between real failures and transient UI instability, improving signal quality in pipelines.
When tests fail, visibility matters. Drizz provides full-fidelity test artifacts for mobile test runs so teams can debug failures quickly.
These include:
Each failure includes a structured explanation of the UI state, the expected action versus the actual state, and why execution did not proceed, replacing raw logs with structured debugging.
Test creation does not require code.
Teams can write and run a full test in under 10 minutes with no setup
This reduces onboarding time and removes dependency on automation engineers.
Drizz supports end-to-end workflows:
Common use cases include validating backend responses against UI behavior, testing real-time data flows, and running complete user journeys without switching tools.
NikahForever achieved 80%+ automation coverage across UI and API workflows in a single platform
Drizz integrates directly into CI/CD pipelines:
Reports include execution status per test, step-level screenshots, and reasoning for any failures.
This removes the need to inspect logs manually.
Default behavior resets the app before and after tests. Continuous mode allows:
You can also execute specific lines within a test, instead of running the full script.
Drizz supports complex scenarios beyond single-app flows:
Commands like OPEN_APP, EXECUTE_API, and SET_GPS enable realistic user journeys.
Drizz focuses on precision over aggressive healing:
In production usage:
Drizz combines multiple capabilities to eliminate the root causes of flaky mobile tests. These include Vision AI-based element identification, natural language test authoring, zero locator dependency, automatic healing and retry logic, AI-driven debugging with reasoning, unified UI and API testing, CI/CD integration with Slack reporting, continuous execution mode, and support for multi-app and location-based workflows.
By removing reliance on brittle selectors, adapting to UI changes in real time, and handling transient failures automatically, self-healing mobile test automation eliminates the primary sources of flakiness: selector breakage, timing issues, and manual maintenance.
Related Content:
Fix flaky mobile tests | Dynamic UI testing with Vision AI | What is Vision AI testing | Book a demo
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