一篇论文被软件工程领域CCF A类期刊TSE录用

Abstract—Unmanned aerial vehicles (UAVs) are increasingly deployed in complex autonomous missions, making systematic safety testing essential for reliable operation. Simulation-based testing provides a practical and risk-free way to assess UAV behavior, but its effectiveness critically depends on generating

diverse, failure-revealing scenarios under tight computational budgets. Existing automated test generation approaches struggle to meet these requirements: objective-driven methods typically optimize a single aggregated risk score and tend to produce clustered, redundant test cases, whereas structure-based heuristics encourage diversity but lack principled guidance and computational efficiency. To better address these challenges, we frame UAV test case generation as a quality–diversity (QD) optimization problem that balances behavioral coverage and failure potential under a fixed evaluation budget. Building on this formulation, we propose TLQD, a behavior-guided two-level QD framework for UAV test case generation. The first level explores a compact two-dimensional behavior space, parameterized by the obstacle–

obstacle angle and the obstacle–environment cross-sectional ratio, to obtain broad behavioral coverage at low computational cost without invoking the simulator. The second level then concentrates the remaining evaluation budget on behaviorally promising regions, using a behavior-based perturbation operator

together with a repulsion-based repair operator to intensify failure discovery, maintain behavioral diversity, and enforce structurally feasible obstacle layouts. Extensive experiments on two UAV testing benchmarks, SBFT 2024 and ICST 2025, show that TLQD consistently outperforms state-of-the-art baselines in both scenario diversity and fault-triggering effectiveness, demonstrating the benefits of behavior-guided two-level QD optimization for efficient and reliable UAV test generation.