In test automation, one of the biggest problems is broken scripts when a small UI change happens. For example, if a button name or locator changes, your tests fail. This is where self-healing tests come in. With the help of AI, they automatically fix broken locators and keep your tests running smoothly.
What Are Self-Healing Tests?
Self-healing tests are automation scripts that can repair themselves when elements change. Instead of failing, they use AI to find the correct element and continue running.
Example:
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Old locator:
button#login -
New locator:
button#signin
The test doesn’t stop—it adapts and finds the right button.
How AI Helps in Self-Healing
AI powers self-healing by:
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Learning element history
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Finding alternative locators when old ones break
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Reducing maintenance time for testers
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Improving test reliability
Benefits of Self-Healing Tests
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Less manual fixing of locators
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Faster releases with fewer delays
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More stable test runs
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Lower long-term maintenance cost
Tools That Support Self-Healing
Some popular tools with AI-driven self-healing are:
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Testim – smart locators and AI insights
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Katalon Studio – built-in healing features
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Applitools – visual AI with healing support
How to Get Started
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Pick a tool or framework that supports AI-driven healing
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Enable locator tracking or fallback selectors
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Integrate reports to log healing actions
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Review AI suggestions to confirm accuracy
Conclusion
Self-healing tests are a big step forward for automation. By using AI, teams save time, reduce flaky tests, and deliver software with more confidence.
