Machine Learning-Based Self-Healing Systems in Software Engineering: Challenges, Techniques, and Future Directions

Abstract

Self-healing systems have emerged as an important advancement in software engineering by enabling software applications to automatically detect, diagnose, and recover from failures without human intervention. These systems improve reliability, minimize downtime, and enhance system resilience, particularly in complex software environments such as cloud computing, distributed architectures, and Internet of Things (IoT) platforms. The integration of machine learning techniques into self-healing systems has significantly improved their ability to predict faults, detect anomalies, and optimize recovery strategies. This study explores the application of machine learning methods, including anomaly detection, predictive maintenance, reinforcement learning, and federated learning, in the development of self-healing software systems. A comparative analysis of different algorithms is presented to evaluate their effectiveness in improving software performance and operational stability. Experimental findings indicate that machine learning-based self-healing systems substantially reduce Mean Time to Repair (MTTR), improve resource utilization, and increase fault detection accuracy. However, several challenges remain, including computational complexity, data quality, model interpretability, and deployment scalability. The study concludes that machine learning has the potential to revolutionize software reliability by enabling adaptive and autonomous recovery systems capable of operating in dynamic environments.