Developers and manufacturers provide configurable control parameters for flight control programs to support various environments and missions, along with suggested ranges for these parameters to ensure flight safety. However, this flexible mechanism can also introduce a vulnerability known as range specification bugs. The vulnerability originates from the evidence that certain combinations of parameter values may affect the drone's physical stability even though its parameters are within the suggested range. The paper introduces a novel system called icsearcher , designed to identify incorrect configurations or unreasonable combinations of parameters and suggest more reasonable ranges for these parameters. icsearcher applies a metaheuristic search algorithm to find configurations with a high probability of driving the drone into unstable states. In particular, icsearcher adopts a machine learning-based predictor to assist the searcher in evaluating the fitness of configuration. Finally, leveraging searched incorrect configurations, icsearcher can summarize the feasible ranges through multi-objective optimization. icsearcher applies a predictor to guide the search, which eliminates the need for realistic/simulation executions when evaluating configurations and further promotes search efficiency. We have carried out experimental evaluations of icsearcher in different control programs. The evaluation results show that the system successfully reports potentially incorrect configurations, of which over $94\%$ leads to unstable states.

中文翻译：

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通过模糊测试检测飞行控制系统中的范围规范错误

开发商和制造商为飞行控制程序提供可配置的控制参数，以支持各种环境和任务，以及这些参数的建议范围，以确保飞行安全。然而，这种灵活的机制也可能引入一个称为范围规范错误的漏洞。该漏洞源自有证据表明，即使参数值在建议范围内，某些参数值组合也可能会影响无人机的物理稳定性。本文介绍了一种名为icsearcher的新颖系统 ，旨在识别不正确的配置或不合理的参数组合，并为这些参数建议更合理的范围。icsearcher应用元启发式搜索算法来查找极有可能使无人机进入不稳定状态的配置。特别是，icsearcher采用基于机器学习的预测器来帮助搜索者评估配置的适合度。最后，利用搜索到的错误配置，icsearcher可以通过多目标优化总结可行范围。icsearcher应用预测器来指导搜索，这在评估配置时消除了对现实/模拟执行的需要，并进一步提高了搜索效率。我们在不同的控制程序中对icsearcher进行了实验评估。评估结果表明，系统成功报告了潜在的错误配置，其中超过$94\%$导致不稳定状态。