AI-Driven Test Automation Framework

The Challenge

Traditional test automation approaches couldn't keep pace with rapid feature development. UI changes frequently broke existing tests, creating a maintenance burden that slowed release velocity and eroded confidence in the test suite.

The Approach

• Architected an AI-backed testing framework that uses machine learning to adapt to UI changes
• Implemented self-healing locators that automatically update when element attributes change
• Built intelligent test prioritization based on code changes and historical failure patterns
• Created visual regression testing with AI-powered diff analysis to reduce false positives

The Outcome

[Placeholder - Needs metrics on test maintenance reduction, coverage improvement, release velocity gains]

Technologies & Tools