2025/04/25 by Mohammad Baqar, Baqar, Mohammad, Rajat Khanda +3 · 1 citation
Computer Science · #Advanced Software Engineering Methodologies #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Software Engineering (cs.SE) #Software Engineering Research #Software System Performance and Reliability
paper · pdf · doi:10.48550/arxiv.2504.20093
openalex publication_date 2025/04/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.