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Automated Identification and Qualitative Characterization of Safety Concerns Reported in UAV Software Platforms

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Názov: Automated Identification and Qualitative Characterization of Safety Concerns Reported in UAV Software Platforms
Autori: Andrea Di Sorbo, Fiorella Zampetti, Aaron Visaggio, Massimiliano Di Penta, Sebastiano Panichella
Prispievatelia: Di Sorbo, Andrea, Zampetti, Fiorella, Visaggio, Aaron, Di Penta, Massimiliano, Panichella, Sebastiano
Zdroj: Transactions on Software Engineering and Methodology
Informácie o vydavateľovi: Association for Computing Machinery (ACM), 2023.
Rok vydania: 2023
Predmety: 0209 industrial biotechnology, Issue Management, Unmanned Aerial Vehicle, Unmanned aerial vehicle, 02 engineering and technology, 006: Spezielle Computerverfahren, 620: Ingenieurwesen, Empirical study, Safety Issue, Issue management, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Safety issue
Popis: Unmanned Aerial Vehicles (UAVs) are nowadays used in a variety of applications. Given the cyber-physical nature of UAVs, software defects in these systems can cause issues with safety-critical implications. An important aspect of the lifecycle of UAV software is to minimize the possibility of harming humans or damaging properties through a continuous process of hazard identification and safety risk management. Specifically, safety-related concerns typically emerge during the operation of UAV systems, reported by end-users and developers in the form of issue reports and pull requests. However, popular UAV systems daily receive tens or hundreds of reports of varying types and quality. To help developers timely identify and triage safety-critical UAV issues, we (i) experiment with automated approaches (previously used for issue classification) for detecting the safety-related matters appearing in the titles and descriptions of issues and pull requests reported in UAV platforms and (ii) propose a categorization of the main hazards and accidents discussed in such issues. Our results (i) show that shallow machine learning (ML)-based approaches can identify safety-related sentences with precision, recall, and F-measure values of about 80%; and (ii) provide a categorization and description of the relationships between safety issue hazards and accidents.
Druh dokumentu: Article
Jazyk: English
ISSN: 1557-7392
1049-331X
DOI: 10.1145/3564821
DOI: 10.21256/zhaw-25758
Rights: URL: https://www.acm.org/publications/policies/copyright_policy#Background
Prístupové číslo: edsair.doi.dedup.....65bff03aeec9399722db9f3c184881a5
Databáza: OpenAIRE
Popis
Abstrakt:Unmanned Aerial Vehicles (UAVs) are nowadays used in a variety of applications. Given the cyber-physical nature of UAVs, software defects in these systems can cause issues with safety-critical implications. An important aspect of the lifecycle of UAV software is to minimize the possibility of harming humans or damaging properties through a continuous process of hazard identification and safety risk management. Specifically, safety-related concerns typically emerge during the operation of UAV systems, reported by end-users and developers in the form of issue reports and pull requests. However, popular UAV systems daily receive tens or hundreds of reports of varying types and quality. To help developers timely identify and triage safety-critical UAV issues, we (i) experiment with automated approaches (previously used for issue classification) for detecting the safety-related matters appearing in the titles and descriptions of issues and pull requests reported in UAV platforms and (ii) propose a categorization of the main hazards and accidents discussed in such issues. Our results (i) show that shallow machine learning (ML)-based approaches can identify safety-related sentences with precision, recall, and F-measure values of about 80%; and (ii) provide a categorization and description of the relationships between safety issue hazards and accidents.
ISSN:15577392
1049331X
DOI:10.1145/3564821