Real-Time Diagnosis of UAV Configuration Parameters Based on Fuzz Testing

As Unmanned Aerial Vehicle (UAV) application scenarios continue to expand, ensuring their operational safety and reliability has become increasingly critical. Fuzz testing is a key technique for UAV flight control software testing. However, traditional fuzz testing methods rely on historical flight logs for analysis, making it difficult to identify configuration errors in a timely manner. In this study, we introduce a real-time monitoring and diagnostic method based on fuzz testing, which dynamically monitors UAV state data, adjusts the testing strategy in real time, and employs fuzz testing to generate more diverse input samples, thereby enabling real-time identification and correction of configuration errors and software defects. Additionally, a lifting pool mechanism is introduced to filter efficient test cases in real time, optimizing the testing process. Experimental results demonstrate that this method significantly enhances UAV safety and reliability without interfering with normal flight missions. Moreover, the test case acceptance rate reaches 83.8%, the test cycle is reduced by 40%, and the coverage of key modules remains at a high level.