一句话：提出面向多模块 UAV 系统的蜕变系统测试框架 MSTU，用于检测模块协同缺陷并给出评估方案。
近年来，基于机器学习的多模块无人机（UAV）系统发展迅速。此类自主系统常面临测试预言缺失问题，已有工作多利用蜕变测试自动生成测试场景，但主要针对单元测试或端到端模型级测试，难以有效覆盖多个模块紧密协作的 UAV 系统。为此，论文提出一种新的蜕变系统测试框架 MSTU，将蜕变测试从单元级提升到系统级，用于检测多模块 UAV 系统中的缺陷。作者还给出初步评估计划，将 MSTU 应用于一个新兴的自主多模块 UAV 系统，以展示该测试框架的可行性。摘要未报告实际实验结果，主要贡献是框架与评估方案。
Recent years have seen a rapid development of machine learning based multi-module unmanned aerial vehicle (UAV) systems. To address the oracle problem in autonomous systems, numerous studies have been conducted to use metamorphic testing to automatically generate test scenes for various modules, e.g., those in self-driving cars. However, as most of the studies are based on unit testing including end-to-end model-based testing, a similar testing approach may not be equally effective for UAV systems where multiple modules are working closely together. Therefore, in this paper, instead of unit testing, we propose a novel metamorphic system testing framework for UAV, named MSTU, to detect the defects in multi-module UAV systems. A preliminary evaluation plan to apply MSTU on an emerging autonomous multi-module UAV system is also presented to demonstrate the feasibility of the proposed testing framework.