# Automated System-level Testing of Unmanned Aerial Systems

arXiv: https://arxiv.org/abs/2403.15857 · HTML: https://mlsanity.com/paper/2403.15857/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 88 references that are in the map.
- First posted on arXiv 23 Mar 2024.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Human-level performance in first-person multiplayer games with population-based deep reinforcement learning](https://mlsanity.com/paper/1807.01281/) (2018)
- [Reinforcement Learning for UAV Attitude Control](https://mlsanity.com/paper/1804.04154/) (2018)
- [Falsification of Cyber-Physical Systems Using Deep Reinforcement Learning](https://mlsanity.com/paper/1805.00200/) (2018)
- [Adaptive Stress Testing of Trajectory Predictions in Flight Management Systems](https://mlsanity.com/paper/2011.02559/) (2020)
- [Avis: In-Situ Model Checking for Unmanned Aerial Vehicles](https://mlsanity.com/paper/2106.14959/) (2021)
- [CDST: A Toolkit for Testing Cockpit Display Systems of Avionics](https://mlsanity.com/paper/2001.07869/) (2020)
- [Understanding Bounding Functions in Safety-Critical UAV Software](https://mlsanity.com/paper/2102.07020/) (2021)
- [SA4U: Practical Static Analysis for Unit Type Error Detection](https://mlsanity.com/paper/2210.09136/) (2022)
- [Efficient Test Data Generation for MC/DC with OCL and Search](https://mlsanity.com/paper/2401.03469/) (2024)

## Built on by (most cited first, 1 of 1)

- [Digital Twin-based Out-of-Distribution Detection in Autonomous Vessels](https://mlsanity.com/paper/2504.19816/) (2025)

## Abstract (arXiv)

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.