AI-Driven Fault Injection Testing: Enhancing System Resilience with Automated Chaos Engineering

Abstract

This paper presents a novel approach to enhancing system resilience through AI-driven fault injection testing, leveraging automated chaos engineering. As modern distributed systems grow in complexity, traditional resilience testing techniques—often limited by static fault models and insufficient adaptability—struggle to expose hidden vulnerabilities under dynamic real-world scenarios. To address these challenges, we propose an intelligent framework that integrates artificial intelligence, specifically reinforcement learning, with automated chaos tools to dynamically generate and execute context-aware fault scenarios. The system continuously learns from observed behaviors, identifies weak points, and adapts its strategies to maximize test coverage and impact. Experimental results demonstrate a 28% improvement in fault detection accuracy and a 35% reduction in system recovery time compared to conventional static methods. Furthermore, the approach generalizes effectively across various cloud-native and microservice-based architectures. This work contributes to the evolution of autonomous resilience testing, offering a scalable and proactive solution for building more robust, self-healing systems in highly dynamic environments.