Fuzzing Mobile Robot Environments for Fast Automated Crash Detection
Fuzzing Mobile Robot Environments for Fast Automated Crash Detection
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DOI：
10.1109/icra48506.2021.9561627
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发表时间：
2021-05
期刊：
影响因子：
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通讯作者：
Trey Woodlief;Sebastian G. Elbaum;K. Sullivan
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文献类型：
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作者：
Trey Woodlief;Sebastian G. Elbaum;K. Sullivan
测试移动机器人既困难又昂贵，而且许多故障都未被发现。在这项工作中，我们探索模糊测试（一种自动测试输入生成技术）是否可以更快地找到移动机器人中引起故障的输入。我们开发了一种简单的模糊测试工具 BASE-FUZZ，以及专门用于移动机器人模糊测试的 PHYS-FUZZ。 PHYS-FUZZ 的独特之处在于它考虑了物理属性，例如机器人尺寸、估计轨迹和影响测量的时间，以指导测试输入生成过程。 PHYS-FUZZ 的评估结果表明，它有潜力加快发现揭示故障的输入场景的速度，在 7 天的测试中比统一随机输入选择快 56.5%，比 BASE-FUZZ 快 7.0%。
Testing mobile robots is difficult and expensive, and many faults go undetected. In this work we explore whether fuzzing, an automated test input generation technique, can more quickly find failure inducing inputs in mobile robots. We developed a simple fuzzing adaptation, BASE-FUZZ, and one specialized for fuzzing mobile robots, PHYS-FUZZ. PHYS-FUZZ is unique in that it accounts for physical attributes such as the robot dimensions, estimated trajectories, and time to impact measures to guide the test input generation process. The results of evaluating PHYS-FUZZ suggest that it has the potential to speed up the discovery of input scenarios that reveal failures, finding 56.5% more than uniform random input selection and 7.0% more than BASE-FUZZ during 7 days of testing.