Anas Shrinah and Chris Bennett (University of Bristol, Trustworthy Systems Laboratory )

We introduce the overall winner of the Uncrewed Aerial Vehicles (UAVs) Testing Competition at the 18th IEEE International Conference on Software Testing, Verification and Validation (ICST) 2025 and the 18th Intl. Workshop on Search-Based and Fuzz Testing (SBFT), and present an extension that leverages genetic algorithms in addition to a low-fidelity UAV path simulator to efficiently produce effective UAV test cases.

Simulation-based testing provides a safe and cost-effective environment for verifying the safety of UAVs. However, identifying effective test suites requires a large number of simulations, which is resource consuming. To address this challenge, we optimise simulation resources using a model-based test generator that efficiently produces effective and diverse test suites. A genetic algorithm further enhances the test generation by employing a Neural Network (NN) as a surrogate fitness function to enable rapid evaluation of test cases. For the NN to make accurate predictions, it must be trained on a large dataset—one that cannot be feasibly generated using computationally intensive High-Fidelity Simulators (HFS). To overcome this, we simplify the PX4 autopilot HFS to develop a Low-Fidelity Simulator (LFS), which can produce the required training data an order of magnitude faster than the HFS.

Key Points:

• We present the overall winner of the Uncrewed Aerial Vehicles (UAVs) Testing Competitions at the 18th IEEE International Conference on Software Testing, Verification and Validation (ICST) 2025 and the 18th Intl. Workshop on Search-Based and Fuzz Testing (SBFT) 2025.
• We explain how to harness the power of a Genetic Algorithm and a Neural Network to efficiently generate diverse and effective UAV test suites.
• We highlight how a High-Fidelity Simulator is simplified into a Low-Fidelity Simulator to speed up training data generation by an order of magnitude while retaining sufficient accuracy for effective model-based test generation.

Dr Anas Shrinah is an Assistant Professor at the Applied Science University in Amman, Jordan, and an Honorary Senior Research Associate with the Trustworthy Systems Laboratory (https://www.bristol.ac.uk/research/groups/trustworthy-systems-laboratory/) at the University of Bristol. His research focuses on leveraging artificial intelligence to generate effective test cases for cyber-physical systems. Anas led the development of the UAV test case generation tool that won first place in SBFT 2025 UAV Testing Competition and was named the overall winner in both the SBFT 2025 and ICST 2025 UAV Testing Competitions.

Anas is a certified Project Management Professional (PMP) with over 16 years of combined experience in academia and industry. He holds a PhD in the verification and validation of planning-based autonomous systems, an MSc in Robotics (with Distinction), as well as a first-class honours BEng in Computer and Automation Engineering.

Dr Chris Bennett is a Senior Research Associate with the Trustworthy Systems Laboratory (https://www.bristol.ac.uk/research/groups/trustworthy-systems-laboratory/) at the University of Bristol, developing machine learning techniques for test-based verification. A chartered engineer with a background in systems engineering for automotive, he worked at Jaguar Land Rover before transitioning into research seven years ago, completing a PhD in Robotics and Autonomous Systems. He has previously worked on projects with Thales UK, examining the role of hybrid autonomy in multi-agent systems, and on the UKRI funded Trustworthy Autonomous Systems project, investigating how trust can be built in artificial intelligence and robotics through system engineering practices. His research interests include test-based verification, system engineering design practices, and multi-agent artificial intelligence.