An Exploratory Investigation of Log Anomalies in Unmanned Aerial Vehicles

Wang, D; Li, S; Xiao, G; Liu, Y; Sui, Y; He, P; Lyu, MR

An Exploratory Investigation of Log Anomalies in Unmanned Aerial Vehicles

Wang, D Li, S Xiao, G Liu, Y Sui, Y

He, P Lyu, MR

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Publisher:: ACM
Publication Type:: Conference Proceeding
Citation:: Proceedings - International Conference on Software Engineering, 2024, pp. 2593-2605
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Wang, D
dc.contributor.author	Li, S
dc.contributor.author	Xiao, G
dc.contributor.author	Liu, Y
dc.contributor.author	Sui, Y https://orcid.org/0000-0002-9510-6574
dc.contributor.author	He, P
dc.contributor.author	Lyu, MR
dc.date	2024-04-14
dc.date.accessioned	2025-03-06T20:59:34Z
dc.date.available	2025-03-06T20:59:34Z
dc.date.issued	2024-01-01
dc.identifier.citation	Proceedings - International Conference on Software Engineering, 2024, pp. 2593-2605
dc.identifier.isbn	979-8-4007-0217-4
dc.identifier.issn	0270-5257
dc.identifier.uri	http://hdl.handle.net/10453/185548
dc.description.abstract	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.
dc.language	en
dc.publisher	ACM
dc.relation.ispartof	Proceedings - International Conference on Software Engineering
dc.relation.ispartof	International Conference on Software Engineering
dc.relation.isbasedon	10.1145/3597503.3639186
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.title	An Exploratory Investigation of Log Anomalies in Unmanned Aerial Vehicles
dc.type	Conference Proceeding
utslib.location.activity	Lisbon Portugal
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Australian Artificial Intelligence Institute (AAII)
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Cyber Security and Privacy (CCSP)
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2025-03-06T20:59:31Z
pubs.finish-date	2024-04-20
pubs.place-of-publication	USA
pubs.publication-status	Published
pubs.start-date	2024-04-14
dc.location	USA

Abstract:

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.

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http://hdl.handle.net/10453/185548