An Exploratory Investigation of Log Anomalies in Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) are becoming increasingly ubiqui-tous in our daily lives. However, like many other complex systems, UAVs are susceptible to software bugs that can lead to abnormal system behaviors and undesirable consequences. It is crucial to study such software bug-induced UAV anom...

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Bibliographic Details
Published in:Proceedings / International Conference on Software Engineering pp. 2593 - 2605
Main Authors: Wang, Dinghua, Li, Shuqing, Xiao, Guanping, Liu, Yepang, Sui, Yulei, He, Pinjia, Lyu, Michael R.
Format: Conference Proceeding
Language:English
Published: ACM 14.04.2024
Subjects:
Autonomous aerial vehicles
Bridges
Code Pattern
Codes
Complex systems
Computer bugs
Crash
Empirical Study
Safety
Software
Software Bug
UAV Anomaly
ISSN:1558-1225
Online Access:Get full text
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Summary:Unmanned aerial vehicles (UAVs) are becoming increasingly ubiqui-tous in our daily lives. However, like many other complex systems, UAVs are susceptible to software bugs that can lead to abnormal system behaviors and undesirable consequences. It is crucial to study such software bug-induced UAV anomalies, which are often mani-fested in flight logs, to help assure the quality and safety of UAV systems. However, there has been limited research on investigating the code-level patterns of software bug-induced UAV anomalies. This impedes the development of effective tools for diagnosing and localizing bugs within UAV system code. To bridge the research gap and deepen our understanding of UAV anomalies, we carried out an empirical study on this subject. We first collected 178 real-world abnormal logs induced by soft-ware bugs in two popular open-source UAV platforms, i.e., PX4 and Ardupilot. We then examined each of these abnormal logs and com-piled their common patterns. In particular, we investigated the most severe anomalies that led to UAV crashes, and identified their features. Based on our empirical findings, we further summarized the challenges of localizing bugs in system code by analyzing anoma-lous UAV flight data, which can offer insights for future research in this field.
ISSN:1558-1225
DOI:10.1145/3597503.3639186