Self-Healing Software Systems: AI-Driven Fault Prediction and Recovery

The issue with modern software systems is that they are becoming extremely complicated and interdependent, which requires fault detection and recovery as key factors for reliability and performance, especially in mission-critical settings. Self-healing computer systems are a new technology that has entered the field as a groundbreaking approach with the ability to automatically monitor, predict, and fix failures on their own. The paper reviews an approach to integrating Artificial Intelligence (AI) in self-healing architectures, specifically through machine learning, semantic reasoning, and bio-inspired strategies to enhance resilience and operational efficiency. 
The paper discusses frameworks for fault prediction, anomaly detection, and automated recovery in different computing systems, such as cloud infrastructures, IoT ecosystems, and healthcare systems, based on recent studies. It also discusses how AI is now being utilized in software testing, log analysis, and cognitive fault diagnosis. The findings indicate that AI not only increases the accuracy of fault management but also delivers predictive and adaptive strategies that outperform traditional reactive models. The review concludes by describing existing challenges and future research directions required to develop reliable self-healing software systems that can be scaled to new levels.