一句话：面向自主系统，利用低保真仿真自动测试，并推断系统模型以增强故障定位与自动修复。
机器人与自主系统正日益与公众互动，其失效可能代价高昂甚至致命。然而，直接对真实系统进行系统性测试成本高且风险大，低成本软件仿真因此成为尽早发现故障的有前景途径。作者早期工作表明，多数缺陷可在低保真仿真环境中复现和发现，并为此构建了一个面向常用 ArduPilot 系统的高层自动化测试框架。在此基础上，本文提出自动推断系统模型的有力表示，并生成测试套件，以增强自动化故障定位性能并描述失效根因；最后，作者计划利用这些方法指导自主系统自动程序修复技术的构建。该研究属于博士研究计划，核心目标是将测试、故障定位与修复自动化整合到自主系统的质量保证流程中。
Robots and autonomous systems are finding their way to interact with the public and failures in these systems could be extremely expensive, even deadly. However, low-cost software-based simulation could be a promising approach to systematically test robotics systems and prevent failures as early as possible. In our early work, we showed that the majority of bugs could actually be reproduced and discovered using low-fidelity simulation environment. We created a high-level framework for automated testing of popular ArduPilot systems. In this work, I propose novel approaches to automatically infer powerful representation of system models, and generate test suites with the purpose of enhancing automated fault localization performance and describing the root cause of failures. Finally, I propose to use those novel approaches to inform the construction of automated program repair techniques for autonomous systems.