plan and execute flight tests

To reconcile the stringent DO-178C Level A safety certification requirements with the escalating complexity of avionics software, this paper proposes an airworthiness-compliant, customized Scrum framework. The method introduces a multidisciplinary Product Owner role, dual acceptance criteria—functional and certification-oriented—separate independent test/documentation teams, and a dedicated Certification Coordinator. It integrates continuous integration/delivery, automated documentation generation, and rigorous configuration management. These innovations enable deep coupling between agile iteration and regulatory compliance. Empirical evaluation demonstrates significant improvements over the traditional waterfall model: a 76% reduction in average requirement effort per engineer, 75% faster defect detection, 78% higher defect resolution efficiency, and over 50% lower defect density—all while fully satisfying DO-178C Level A certification objectives.

In outdoor UAV search-and-rescue testing for aerospace applications, challenges include highly dynamic environments, irreproducible scenarios, and inadequate test guidance. To address these, this paper proposes FiTS (Field Testing Management based on Scenarios), a novel scenario-driven testing methodology. FiTS innovatively integrates scenario-based requirements engineering with Behavior-Driven Development (BDD) to establish a comprehensive test framework supporting dynamic environmental perception, role-based task allocation, structured test design, and iterative optimization. Through formal scenario modeling and role-oriented test specification, FiTS enables adaptive, reusable, and traceable test execution. Empirical validation across three representative search-and-rescue use cases demonstrates that FiTS significantly improves test execution efficiency and data acquisition quality. Furthermore, expert evaluation by three senior developers confirms substantial enhancements in test traceability and analytical capability for post-test data interpretation.

To address safety violations caused by frequent unauthorized incursions of small Unmanned Aircraft Systems (sUAS) into controlled airspace and the inefficiency of manual safety assurance, this paper proposes SafeSPLE—a novel approach that pioneers the application of Software Product Line Engineering (SPLE) to safety case development. SafeSPLE integrates hazard analysis with feature modeling to construct a parameterized safety case template; domain-specific safety claims are then automatically instantiated and generated via product line configuration tailored to individual flight missions. This enables customizable, scalable, and regulation-compliant airspace access control while significantly improving assessment consistency and efficiency. Empirical evaluation demonstrates that SafeSPLE efficiently produces regulatory-compliant safety cases, offering a reusable, verifiable technical foundation for sUAS integration into controlled airspace.

Safety-critical small Unmanned Aircraft Systems (sUAS) lack systematic, standardized testing processes that are tightly integrated with safety analysis. Method: This paper proposes a requirement-driven coupled testing framework, introducing the novel triadic paradigm of “requirements–simulation testing–safety analysis.” It employs formal requirement modeling with bidirectional traceability, a simulation–hardware-in-the-loop cooperative testing architecture, scenario-driven test case generation, and deep integration of safety analysis methods (e.g., Fault Tree Analysis and System-Theoretic Process Analysis). Contribution/Results: Evaluated on an sUAS case study, the framework significantly improves simulation fidelity coverage and requirement coverage, enables end-to-end safety evidence generation, fills the gap in standardized sUAS testing procedures, and delivers reproducible, verifiable testing assets to support airworthiness certification.

Ensuring simultaneous obstacle avoidance and geofence compliance for fixed-wing UAVs during flight poses significant safety challenges under nonlinear kinematic constraints. Method: This paper proposes a real-time assurance (RTA) framework based on control barrier functions (CBFs), specifically designed for nonlinear kinematic models of fixed-wing UAVs. We systematically formulate and compare multiple CBF variants to jointly enforce collision avoidance and geofence constraints in a unified, formal safety guarantee. The architecture operates at the command layer, dynamically modifying control inputs to ensure closed-loop safety under both constraints. Contribution/Results: We provide rigorous theoretical proofs establishing formal safety guarantees. Extensive validation—across both kinematic and high-fidelity dynamical simulations—demonstrates zero constraint violations and zero collisions. The approach significantly enhances the verifiable safety assurance capability of fixed-wing platforms operating in complex, constrained airspace.

Aerodynamic parameter uncertainty in flight testing poses significant maneuver safety risks, yet existing abort criteria lack theoretical guarantees and struggle to handle dynamic uncertainties. Method: We propose a data-driven real-time safety alerting framework comprising three stages: trajectory prediction, nearest-neighbor safety classification, and conformal prediction–based calibration—enabling reliable quantification of short-term safety risk. Contribution/Results: To our knowledge, this is the first work to integrate conformal prediction into flight safety classification calibration, providing rigorous coverage probability guarantees under user-specified confidence levels and enabling cross-configuration generalization. Experiments on uncertain flight dynamic models demonstrate that the system accurately identifies critical hazardous scenarios, achieving significantly higher risk anticipation accuracy than baseline methods while strictly satisfying theoretical coverage requirements.

This study addresses the lack of interactive program execution and safety compliance evaluation in aviation large model agents by constructing a virtual cockpit environment alongside a dual-layer benchmark. We propose a safety-gated framework that translates natural language instructions into executable state transitions, enabling joint verification of task completion and trajectory safety. Experimental results demonstrate that the optimal model achieves a 72.6% success rate, revealing the inherent limitations of static knowledge in guaranteeing dynamic execution. Furthermore, this work identifies critical failure modes within long-horizon tasks, thereby establishing a systematic paradigm for the safety assessment of intelligent aviation agents.

本文通过监测管制员与飞行员的语音交流、监视数据和机载观察，解决空中交通控制程序执行中的安全问题。

为解决安全关键飞行预测中现有评估协议的不足，提出FLY-EVAL++，结合确定性验证与物理可行性、安全性约束进行多维度评分。

This study addresses the challenge that drone control code generated by large language models (LLMs), while syntactically correct, frequently violates task intentions and physical constraints. To overcome this limitation, this work proposes an agent-assisted middleware framework featuring a novel dual-layer verification architecture that integrates program-level and execution-level agents. By combining static analysis with simulated trajectory evaluation, the framework precisely localizes errors through staged verification and provides structured feedback to guide iterative code refinement. Experimental results demonstrate that the proposed approach increases navigation success rates from 55% to 95% and overall mission success rates from 34% to 88%, substantially enhancing the reliability and safety of LLM-generated drone control code.