Aug 25, 2025 3 min read AIandML

TTL Episode 5 : Self-Healing Tests with AI: Making Automation Smarter

Podcast Blog Post: Episode 5
Title: Self-Healing Tests with AI: Making Automation Smarter

In the fifth episode of our podcast series on AI in software testing, we dive deep into one of the most transformative innovations in the QA landscape: Self-Healing Tests powered by AI. This episode unpacks the what, why, and how of self-healing automation, shedding light on the tools, technologies, and real-world impact for test engineers.

What Are Self-Healing Tests?

Self-healing tests refer to automated test scripts that can detect when a test fails due to changes in the UI (like an updated element ID or label), automatically locate the correct element using AI, and continue execution without manual intervention.

Problem it Solves:

Test flakiness from minor UI updates
High maintenance burden (sometimes up to 80% of QA effort)
Blocked releases due to fragile test scripts

Value Delivered:

Improved test stability
Reduced maintenance
Faster CI/CD pipelines

How Does Self-Healing Work?

Self-healing uses AI/ML techniques to mimic the intelligence of a human tester by understanding the intent behind a UI element. Here's a simplified workflow:

Element Fingerprinting: The tool captures multiple attributes of each UI element (tag name, text, position, color, sibling elements, etc.) to create a robust profile.
Error Detection: When a test fails due to a broken locator, the system analyzes the issue.
Healing Mechanism: It compares the broken element with all current page elements and calculates a similarity score.
Automatic Fix: If a suitable match is found, the test is updated and proceeds.
Learning Loop: AI refines its decision-making as it handles more such failures.

Tools Powering Self-Healing

The episode highlights several tools and platforms that offer self-healing capabilities:

Applitools (Visual AI combined with test maintenance)
Helenium (Open-source solution for self-healing locators)
testRigor (Simplified scripting with built-in healing)
Mabl, Functionize, Catalon Studio, DogQ, LambdaTest

Each has its own approach to healing, be it locator intelligence, visual cues, or NLP-based text interpretation.

Real-World Use Cases

The episode presents relatable scenarios:

An eCommerce site where the button label changes from "Buy Now" to "Add to Cart," yet tests continue without breaking.
A banking app that adjusts when column names or labels are dynamically updated.
A Salesforce-heavy environment where UI updates happen frequently, and AI ensures test consistency.

Measurable Impact

85% reduction in test maintenance effort
82% improvement in test reliability
48% increase in test coverage
Flaky test rates dropping from 22% to below 4%
Faster releases and reduced human intervention

These improvements make a strong case for adopting self-healing, especially in agile environments.

When Should You Use Self-Healing?

Self-healing is ideal if:

Your app UI changes frequently
You're working in Agile or DevOps teams
Your team struggles with flaky or brittle tests
You're using dynamic UI components like in React or Angular

Getting Started: Best Practices

Pick the right tool: Ensure it's compatible with your existing stack.
Start with high-change areas: Focus on screens/components that frequently break.
Monitor healing decisions: Always verify AI-generated fixes.
Educate your team: Help them understand how to work with and trust AI in tests.

Final Thoughts

Self-healing tests mark a new era in test automation where AI truly becomes a co-pilot. It not only saves time but also empowers QA teams to focus on more impactful tasks like exploratory testing and quality strategy.

In our next episode, we’ll explore AI-driven visual testing and how it goes beyond pixel comparison to understand visual intent. Stay tuned!

Want to go deeper?
Try integrating a tool like Applitools or testRigor into your test pipeline to experience healing in action.