Unmanned aerial systems (UAS) have a large number of applications in civil and military domains. 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 systems, including software systems. The current industrial practice is to manually create test scenarios, manually or automatically execute these scenarios using simulators, and manual evaluation of the outcomes. A fundamental part of system-level testing of such systems is the simulation of environmental context. The test scenarios typically consist of setting certain environment conditions and testing the system under test in these settings. The state-of-the-art approaches available for this purpose also require manual test scenario development and manual test evaluation. In this research work, we propose an 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. We develop a toolset to support automation. We perform a pilot experiment using a widely-used open-source autopilot, ArduPilot. The preliminary results show that the AITester is effective and efficient in violating autopilot expected behavior.

@inproceedings{paperbot6517,
  title = {Automated Approach for System-level Testing of Unmanned Aerial Systems},
  author = {Hassan Sartaj},
  booktitle = {ASE},
  year = {2021},
  doi = {10.1109/ase51524.2021.9678902}
}