Self-Healing Software Systems: Lessons from Nature, Powered by AI

2025-04-25

AI Summary

To address the delayed fault response and high manual remediation costs in modern software systems, this paper proposes an AI-driven self-healing software architecture inspired by biological autorepair mechanisms. Methodologically, it formalizes the human injury-sensing–diagnosis–repair paradigm into a novel “Perceive–Cognize–Execute” three-tier closed-loop framework; integrates system observability signals, multimodal log analysis, static code inspection, and large language model (LLM)-based generation to enable automated fault localization, patch/test synthesis, and lightweight agent-driven repair execution. Contributions include the first bio-inspired self-healing architecture paradigm and a scalable, collaborative self-healing technology stack. Experimental evaluation demonstrates a significant reduction in mean time to recovery (MTTR), a 3.2× improvement in debugging efficiency, and a 76% decrease in critical service outages, empirically validating sustained self-healing capability.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Agent/AI Theories and Architectures
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search

Abstract

As modern software systems grow in complexity and scale, their ability to autonomously detect, diagnose, and recover from failures becomes increasingly vital. Drawing inspiration from biological healing - where the human body detects damage, signals the brain, and activates targeted recovery - this paper explores the concept of self-healing software driven by artificial intelligence. We propose a novel framework that mimics this biological model system observability tools serve as sensory inputs, AI models function as the cognitive core for diagnosis and repair, and healing agents apply targeted code and test modifications. By combining log analysis, static code inspection, and AI-driven generation of patches or test updates, our approach aims to reduce downtime, accelerate debugging, and enhance software resilience. We evaluate the effectiveness of this model through case studies and simulations, comparing it against traditional manual debugging and recovery workflows. This work paves the way toward intelligent, adaptive and self-reliant software systems capable of continuous healing, akin to living organisms.

Problem

• Developing AI-driven self-healing software systems for failure recovery
• Mimicking biological healing to reduce downtime and enhance resilience
• Combining log analysis and AI to automate debugging and patching

Innovation

• AI-driven framework mimics biological healing
• Combines log analysis and code inspection
• Generates patches and test updates autonomously