The increasing deployment of small Uncrewed Aerial Systems (sUAS) in diverse and often safety-critical environments demands rigorous validation of onboard decision logic under various conditions. In this paper, we present SaFUZZ, a state-aware fuzzing pipeline that validates core behavior associated with state transitions, automated failsafes, and human operator interactions in sUAS applications operating under various timing conditions and environmental disturbances. We create fuzzing specifications to detect behavioral deviations, and then dynamically generate associated Fault Trees to visualize states, modes, and environmental factors that contribute to the failure, thereby helping project stakeholders to analyze the failure and identify its root causes. We validated SaFUZZ against a real-world sUAS system and were able to identify several points of failure not previously detected by the system's development team. The fuzzing was conducted in a high-fidelity simulation environment, and outcomes were validated on physical sUAS in a real-world field testing setting. The findings from the study demonstrated SaFUZZ's ability to provide a practical and scalable approach to uncovering diverse state transition failures in a real-world sUAS application.

翻译：随着小型无人驾驶航空系统（sUAS）在多样化且通常涉及安全关键的环境中日益广泛部署，亟需对其机载决策逻辑在各种条件下的运行进行严格验证。本文提出SaFUZZ——一种状态感知的模糊测试流程，用于验证在不同时序条件与环境干扰下运行的sUAS应用中，与状态转换、自动故障安全机制及人机交互相关的核心行为。我们通过构建模糊测试规范来检测行为偏差，并动态生成相应的故障树以可视化导致故障的状态、模式及环境因素，从而帮助项目相关人员分析故障并定位其根本原因。我们在真实sUAS系统上对SaFUZZ进行了验证，成功识别出多个系统开发团队未曾发现的故障点。测试在高保真仿真环境中进行，其结果在真实外场测试环境下通过物理sUAS平台得到验证。研究结果表明，SaFUZZ能够为揭示实际sUAS应用中多样化的状态转换故障提供实用且可扩展的解决方案。