An Exploratory Investigation of Log Anomalies in Unmanned Aerial Vehicles
Unmanned aerial vehicles (UAVs) are becoming increasingly ubiquitous 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 manifested 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 software bugs in two popular open-source UAV platforms, i.e., PX4 and Ardupilot. We then examined each of these abnormal logs and compiled 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 anomalous UAV flight data, which can offer insights for future research in this field.
Fri 19 AprDisplayed time zone: Lisbon change
14:00 - 15:30 | Analytics 5Research Track / Journal-first Papers at Amália Rodrigues Chair(s): Sridhar Chimalakonda Indian Institute of Technology, Tirupati | ||
14:00 15mTalk | An Exploratory Investigation of Log Anomalies in Unmanned Aerial Vehicles Research Track Dinghua Wang , Shuqing Li The Chinese University of Hong Kong, Guanping Xiao Nanjing University of Aeronautics and Astronautics, Yepang Liu Southern University of Science and Technology, Yulei Sui UNSW, Pinjia He Chinese University of Hong Kong, Shenzhen, Michael Lyu The Chinese University of Hong Kong | ||
14:15 15mTalk | ModuleGuard: Understanding and Detecting Module Conflicts in Python Ecosystem Research Track Ruofan Zhu Zhejiang University, Xingyu Wang Zhejiang University, Chengwei Liu Nanyang Technological University, Zhengzi Xu Nanyang Technological University, Wenbo Shen Zhejiang University, China, Rui Chang Zhejiang University, Yang Liu Nanyang Technological University | ||
14:30 15mTalk | Empirical Analysis of Vulnerabilities Life Cycle in Golang Ecosystem Research Track Jinchang Hu , Lyuye Zhang Nanyang Technological University, Chengwei Liu Nanyang Technological University, Sen Yang Academy of Military Science, Song Huang Army Engineering University of PLA, Yang Liu Nanyang Technological University | ||
14:45 15mTalk | Fine-SE: Integrating Semantic Features and Expert Features for Software Effort Estimation Research Track Yue Li Nanjing University, Zhong Ren State Key Laboratory of Novel Software Technology, Software Institute, Nanjing University Nanjing, Jiangsu, China, Zhiqi Wang State Key Laboratory of Novel Software Technology, Software Institute, Nanjing University Nanjing, Jiangsu, China, Lanxin Yang Nanjing University, Liming Dong Nanjing University, He Zhang Nanjing University | ||
15:00 7mTalk | Concretization of Abstract Traffic Scene Specifications Using Metaheuristic Search Journal-first Papers Aren Babikian McGill University, Oszkár Semeráth Budapest University of Technology and Economics, Daniel Varro Linköping University / McGill University | ||
15:07 7mTalk | Technical leverage analysis in the Python ecosystem Journal-first Papers Ranindya Paramitha University of Trento, Fabio Massacci University of Trento; Vrije Universiteit Amsterdam | ||
15:14 7mTalk | Automated Mapping of Adaptive App GUIs from Phones to TVs Journal-first Papers Han Hu Faculty of Information Technology, Monash University, ruiqi dong Swinburne University of Technology, John Grundy Monash University, Thai Minh Nguyen Monash University, huaxiao liu Jilin University, Chunyang Chen Technical University of Munich (TUM) Link to publication DOI Pre-print | ||
15:21 7mTalk | Assessing the Early Bird Heuristic (for Predicting Project Quality) Journal-first Papers Link to publication DOI Pre-print | ||