Smart-Asli AI healing for automation testing on real devices

Locator failure is a significant problem for automation tests. Little UI changes such as changing names and also updated attributes or hierarchy of new elements can cause fragile scripts to break so that it leads to wasted debugging hours plus scaly build and even a slower release cycle.

Lambdatest introduced Smart Heal to complete this for automation tests on real devices. The AI/ML algorithm gives strength to this new feature. Smart Heal automatically detects, analysis and recovery of the locator failure during Runtime, ensuring your automation script remains tough, even when UI changes.

Challenges Before Smart Heal

Before Smart Heal, Locator failure is a recurring problem for the QA and Dev team:

• Fragile test: Even small UI changes such as changing the name of the button or shifting the script broke the element, causing frequent failures.
• Time consuming maintenance: Examiners spend hours to update and improve location seekers manually after each release.
• Pipeline instability: Ci/CD buildings often fail due to locator search problems, creating false negative and slow spread.
• Delayed release: The team must stop or run back the pipeline only to overcome the intrinsass test, increase the release schedule.
• Bad visibility: When a locator fails, there is no automatic context or suggestion, debugging means digging through the log line by row.

The main benefit of AIIFT’s smart heal for automation tests on real devices

• Tough Test Automation: Maintain stable automation with automatic healing locator failures during Runtime, even when the application undergos frequent changes in UI or DOM.
• Faster shipping: Preventing damage to tests in a fast moving release cycle, allowing faster Go-to-Market without reducing quality.
• Reducing maintenance efforts: Minimizing the time of the team spent on improving the broken script manually, letting them focus on expanding the scope of the test instead.
• Increased reliability of CI/CD: Ensuring a smoother pipe execution by automatically handling scaly locator problems, reducing false negatives.
• Transparent healing log: Provide full visibility with detailed logs, cured locator mapping, and screenshots before and after the dashboard.
• Original Debugging AI: Providing intelligent advice when healing is not possible, helping examiners strengthen location seekers proactively.
• Real Reduan Accuracy: Healing occurs in the cloud of real device Lambdatest, ensuring validation improvements in the environment that matches the condition of the final user.
• Sustainable Baseline Renewal: Maintaining a fresh baseline locator by learning from every successful path, making healing mechanism smarter than time to time.

How smart heal works

1. Creation of baseline

Smart Heal requires at least one successful trial as a base line. During this running, all location -searching elements were captured and stored. This baseline acts as a foundation for future healing efforts.

Tip: Make sure the project and your test name remain consistent all running for the baseline application.

2. Baseline update

After every successful run, Smart Heal updates the baseline with the most recent build, keeping it in harmony with the latest UI conditions.

3. Detection and healing

If an element is lost in running the next, Smart Heal uses AI to analyze attributes, dom -hierarchy, and visual cues to find the most valid compatibility.

4. Try again with a cured locator

After the match is found, this step is trying again automatically with a cured locator, allows the test flow to continue smoothly. Both the original and cured locators are recorded for transparency.

5. Fallback and Suggestions

If Smart Heal cannot heal with confidence, the advice that AI moved recorded on the dashboard to help you strengthen your location seeker.

Test reviews cured in the Lambdatest dashboard

• Log healing action: Detailed log access that shows the original locator seeker and heal, gives you full visibility into what is fixed during the escape.
• Screen catch before and after: See screenshots side by side from UI before and after healing the locator, helping you understand how the healing process works.
• Elements that are cured and not cured: Easy to filter and see the test where smart heal is applied. The healed element is highlighted, while failure that does not heal is characterized by red.
• AI original insight: When Smart Heal cannot confidently heal a location seeker, it provides advice and insights that are driven by AI in the dashboard to help you improve location seekers.
• SUMMARY SESSION: Arwah above the cured test builds to see a summary of healing actions carried out during the session, making debugging faster and more efficient.

Get initial access

Smart Heal is currently in a closed beta and evolved quickly with user feedback. If you want to try it for your team:

👉 reach through our 24 × 7 chat or our email at support@lambdatest.com.
👉 After being released publicly, Smart Heal will be included under AI credit.

Make your automation smarter, faster, and more resilient with Lambdatest Smart Heal.