首次系统研究物理引擎软件中的物理异常，揭示其表现与检测难题。
Runtime Failure Hunting for Physics Engine Based Software Systems: How Far Can We Go?
- 构建物理失败类型分类体系，归纳多种异常表现。
- 评估深度学习、提示工程等方法在检测复杂物理错误上的效果。
- 基于开发者反馈提出改进检测方案的实用建议。
物理引擎（PE）是模拟物理交互的核心软件框架，广泛应用于娱乐到自动驾驶、医疗机器人等关键系统。尽管重要，但物理引擎常出现物理行为偏差（即物理失败），影响可靠性、用户体验，甚至引发严重事故。当前针对基于物理引擎软件的测试方法不足，多依赖白盒访问且仅关注崩溃检测，忽视语义复杂的物理错误。本文首次开展大规模实证研究，探索三类核心问题：物理失败的表现形式、现有检测技术的有效性、开发者的实际感知。贡献包括：（1）提出物理失败表现的分类体系；（2）全面评估深度学习、提示驱动及多模态大模型等检测方法；（3）基于开发者访谈提炼出可操作的改进洞察。为支持后续研究，论文公开了数据集PhysiXFails及相关代码与材料。
原文摘要 · Abstract (English)
Physics Engines (PEs) are fundamental software frameworks that simulate physical interactions in applications ranging from entertainment to safety-critical systems. Despite their importance, PEs suffer from physics failures, deviations from expected physical behaviors that can compromise software reliability, degrade user experience, and potentially cause critical failures in autonomous vehicles or medical robotics. Current testing approaches for PE-based software are inadequate, typically requiring white-box access and focusing on crash detection rather than semantically complex physics failures. This paper presents the first large-scale empirical study characterizing physics failures in PE-based software. We investigate three research questions addressing the manifestations of physics failures, the effectiveness of detection techniques, and developer perceptions of current detection practices. Our contributions include: (1) a taxonomy of physics failure manifestations; (2) a comprehensive evaluation of detection methods including deep learning, prompt-based techniques, and large multimodal models; and (3) actionable insights from developer experiences for improving detection approaches. To support future research, we release PhysiXFails, code, and other materials at https://sites.google.com/view/physics-failure-detection.
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