Self-Healing Systems in Software Engineering: A Machine Learning Approach

Abstract

Abstract: Self-healing systems have emerged as a promising solution in software engineering, aiming to automatically detect, diagnose, and rectify issues within complex software infrastructures. Leveraging machine learning techniques, these systems offer a way to enhance reliability, reduce downtime, and improve user satisfaction by autonomously addressing failures and performance degradation. This study explores the integration of machine learning algorithms, such as anomaly detection, reinforcement learning, and predictive maintenance, to design self-healing mechanisms for modern software systems. A comprehensive evaluation of various machine learning approaches is conducted to identify their effectiveness in different software environments, such as cloud computing, distributed systems, and IoT-based platforms. The research also highlights the importance of data-driven models for enabling accurate predictions of system failures and optimizing the recovery process. Through experimental validation, the study demonstrates that machine learning-based self-healing systems can achieve a significant reduction in mean time to repair (MTTR) and improve the overall resilience of software architectures. However, challenges such as data quality, model interpretability, and computational overhead remain critical considerations for real-world deployment. The findings contribute to the ongoing research on building autonomous systems that can adapt to dynamic changes, providing a foundation for future advancements in self-managing software solutions.