AI Powered Predictive Self-Healing Software Systems: A Review
Abstract
Modern software, networks, operating systems and factories have grown too complex to be repaired by hand. Self-healing systems try to notice a problem, find its cause and fix it without waiting for a person. This paper reviews four recent studies that attack this goal at four different levels: finding faulty lines of code with a bee-colony and chaotic genetic search, choosing repair actions for software-defined networks with reinforcement learning, repairing Android, Linux, macOS and Windows machines from system logs, and a large survey of fault diagnosis and self-healing in smart manufacturing. We explain each study in plain language, compare their methods, data and results side by side, and point out where their numbers or claims need careful reading. All four follow the same basic loop of monitor, detect, diagnose and repair, and all report clear gains over simple baselines. However, most results come from simulations, emulators or benchmarks rather than live systems, multiple simultaneous faults and slowly growing faults are handled weakly, and almost nothing acts before a failure happens. We close with research gaps and directions for predictive, safe and explainable self-healing software.
