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Building a Self-Healing Test Prototype
In this hands-on lab, you will build a self-healing UI test prototype that continues to pass after a minor UI change. You will analyze a fragile UI test, add a simple model-driven selector chooser, and compare maintenance effort before and after. The lab uses a local sample web page, a pre-installed headless browser, and Python-based test utilities. By the end, you will have a working self-healing test that reduces flakiness and the need for manual fixes.
Lab Info
Table of Contents
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Challenge
Analyze a Brittle Test Case to Identify Points of Failure
You will run a provided UI regression test against two versions of a local sample page, observe how a fragile locator breaks after a small DOM change, and capture a clear failure fingerprint that explains exactly what changed and what manual fix would be needed.
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Challenge
Implement a Self-Healing Mechanism Using a Simple ML Approach
You will define multiple candidate selectors for the same element, use a lightweight model or scoring logic based on historical selector outcomes, and plug it into a Python-based self-healing helper so the test can automatically fall back to more reliable locators when the primary one fails.
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Challenge
Demonstrate and Quantify the Value of a Self-Healing Test
You will run both the original brittle test and the updated self-healing test against the changed UI, compare their results and logs, and create a short Markdown summary that highlights reduced manual locator fixes, improved stability, and lower risk of pipeline delays.
Real skill practice before real-world application
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