用AI模拟生物自愈机制，让软件自动修复故障。
Self-Healing Software Systems: Lessons from Nature, Powered by AI
- 借鉴人体自愈机制，用AI分析日志与代码实现智能诊断。
- 实验显示该系统可显著缩短故障恢复时间，降低人工干预。
- 适合追求高可用性、自动化运维的大型软件团队使用。
随着现代软件系统日益复杂和规模化，其自主检测、诊断并恢复故障的能力变得愈发关键。本文受生物自愈机制启发——人体感知损伤、向大脑发出信号，并激活针对性修复——提出一种由人工智能驱动的自愈软件框架。该框架将系统可观测性工具作为感官输入，AI模型作为诊断与修复的认知核心，修复代理执行针对性的代码或测试修改。通过结合日志分析、静态代码检查以及AI生成补丁或测试更新，该方法旨在减少停机时间、加速调试过程并提升软件韧性。我们通过案例研究与仿真评估了该模型的有效性，对比传统手动调试与恢复流程。本工作为构建具备持续自愈能力、类生命体般智能适应的软件系统铺平了道路。
原文摘要 · Abstract (English)
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.
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