Next-Generation Resiliency: Evaluating Ai-Augmented Self-Healing Automation Frameworks

Abstract

Modern software development practices require constant revalidation of changing UIs, but customary test automation frameworks are brittle in the face of such dynamic change. Maintenance of the test suite is the most costly part of any automation project. Customary automation patterns used by the industry tend to utilize static locators, which require meaningful manual maintenance if the underlying UI changes. Generally, machine learning on the automation frameworks set the basis for self-healing characteristics through smart object recognition models and self-adaptive algorithms that heal broken locators automatically. Empirical studies have shown the decreased maintenance costs with machine learning over the customarily used statically defined locators and the need for agentic automation architecture. For screenshots, visual pattern recognition techniques are used to control the program by simulating keyboard and mouse events. For DOM elements, multi-property analysis techniques extract a set of structural similarity and behavioral properties and use them to generate a weighted score. Reinforcement learning models are used for finding optimal corrections using experience replay learning and a deep Q-network architecture across UI, API, and database layers. Fault exposing potential can also be used in defect predictive systems for test case prioritization based on efficiency while keeping the defect detection coverage. The reliability of maintainability prediction can be examined through historical software metric measurements towards predictive systems that ascertain locator fragility before an execution-cycle failure.