Artificial Intelligence for Self-Healing Automation Testing Frameworks: Real-Time Fault Prediction and Recovery
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Abstract
This research explored real-time fault prediction and recovery using artificial intelligence (AI) to develop a healing automation testing framework. Despite the growing importance of software quality, most automation testing frameworks need help predicting and recovering from faults efficiently. This study, leveraging AI, notably machine learning algorithms, seeks to increase the resilience and adaptivity of testing frameworks through self-healing mechanisms.
This research starts with a thorough review of current automation testing practices and limitations in existing testing systems. Then, it shows how AI can play a role in software testing, with some perspectives on how machine learning can strengthen fault detection and prediction accuracy. With predictive maintenance strategies and data analysis methods, AI is explored, methods which allow mean but premature detection and prevention of problems.
We focus on developing automated recovery processes that adjust tests dynamically according to real-time data. Case studies are presented to show actual applications of self-healing mechanisms in different software environments. As a result, the research defines technical challenges (such as AI integration and scalability) and implementation barriers (economic organizational).
This research has important implications for industry stakeholders and has practical recommendations regarding adopting AI-driven self-healing frameworks. Finally, this paper concludes with the possibility of an automated future where AI is critical in changing automation testing from a less effective practice to a more efficient one.
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