Self-Healing Test Automation Using Deep Learning Models

Abstract

Self-healing test automation represents a paradigm shift in software quality assurance by leveraging artificial intelligence and deep learning models to create resilient testing frameworks that automatically adapt to application changes. This article explores the architectural components and implementation strategies for self-healing test automation, focusing on how Convolutional Neural Networks enable dynamic UI element recognition while Recurrent Neural Networks facilitate sequence prediction for proactive test adaptation. We examine the technical underpinnings of dynamic locator identification, anomaly detection, and adaptive test case management, providing practical implementation guidance for development teams. The discussion encompasses performance evaluation methodologies and emerging trends, ultimately demonstrating how self-healing mechanisms significantly reduce maintenance efforts, enhance test reliability, and accelerate development cycles in the rapidly evolving landscape of modern software applications.