Unmanned aerial systems (UAS) rely on various avionics systems that are
safety-critical and mission-critical. A major requirement of international
safety standards is to perform rigorous system-level testing of avionics
software systems. The current industrial practice is to manually create test
scenarios, manually/automatically execute these scenarios using simulators, and
manually evaluate outcomes. The test scenarios typically consist of setting
certain flight or environment conditions and testing the system under test in
these settings. The state-of-the-art approaches for this purpose also require
manual test scenario development and evaluation. In this paper, we propose a
novel approach to automate the system-level testing of the UAS. The proposed
approach (AITester) utilizes model-based testing and artificial intelligence
(AI) techniques to automatically generate, execute, and evaluate various test
scenarios. The test scenarios are generated on the fly, i.e., during test
execution based on the environmental context at runtime. The approach is
supported by a toolset. We empirically evaluate the proposed approach on two
core components of UAS, an autopilot system of an unmanned aerial vehicle (UAV)
and cockpit display systems (CDS) of the ground control station (GCS). The
results show that the AITester effectively generates test scenarios causing
deviations from the expected behavior of the UAV autopilot and reveals
potential flaws in the GCS-CDS.

本文提出了一种利用模型测试和人工智能技术自动化生成、执行和评估各种测试场景的无人机系统级测试的新方法。通过对无人机自动驾驶系统和地面控制站驾驶舱显示系统的核心组件进行实证评估，结果显示该方法有效地生成了导致无人机自动驾驶仪行为异常的测试场景，并揭示了地面控制站驾驶舱显示系统的潜在缺陷。

无人机系统级测试的自动化

Automated System-level Testing of Unmanned Aerial Systems

System-level testing of avionics software systems requires compliance with
different international safety standards such as DO-178C. An important
consideration of the avionics industry is automated test data generation
according to the criteria suggested by safety standards. One of the recommended
criteria by DO-178C is the modified condition/decision coverage (MC/DC)
criterion. The current model-based test data generation approaches use
constraints written in Object Constraint Language (OCL), and apply search
techniques to generate test data. These approaches either do not support MC/DC
criterion or suffer from performance issues while generating test data for
large-scale avionics systems. In this paper, we propose an effective way to
automate MC/DC test data generation during model-based testing. We develop a
strategy that utilizes case-based reasoning (CBR) and range reduction
heuristics designed to solve MC/DC-tailored OCL constraints. We performed an
empirical study to compare our proposed strategy for MC/DC test data generation
using CBR, range reduction, both CBR and range reduction, with an original
search algorithm, and random search. We also empirically compared our strategy
with existing constraint-solving approaches. The results show that both CBR and
range reduction for MC/DC test data generation outperform the baseline
approach. Moreover, the combination of both CBR and range reduction for MC/DC
test data generation is an effective approach compared to existing constraint
solvers.

本文提出了一种有效的自动化修改条件 / 决策覆盖测试数据生成方法，利用基于案例推理和范围缩减启发式技术解决了修改条件 / 决策覆盖定制的对象约束语言约束问题，实验证明该方法优于现有的约束求解方法。