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https://doi.org/10.21256/zhaw-25758| Publication type: | Article in scientific journal |
| Type of review: | Peer review (publication) |
| Title: | Automated identification and qualitative characterization of safety concerns reported in UAV software platforms |
| Authors: | Di Sorbo, Andrea Zampetti, Fiorella Visaggio, Corrado A. Di Penta, Massimiliano Panichella, Sebastiano |
| et. al: | No |
| DOI: | 10.1145/3564821 10.21256/zhaw-25758 |
| Published in: | ACM Transactions on Software Engineering and Methodology |
| Volume(Issue): | 32 |
| Issue: | 3 |
| Page(s): | 67 |
| Issue Date: | 2022 |
| Publisher / Ed. Institution: | Association for Computing Machinery |
| ISSN: | 1049-331X 1557-7392 |
| Language: | English |
| Subjects: | Unmanned aerial vehicle; Issue management; Safety issue; Machine learning; Empirical study |
| Subject (DDC): | 006: Special computer methods 620: Engineering |
| Abstract: | 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 identifying and triaging 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-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. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/25758 |
| Related research data: | https://doi.org/10.5281/zenodo.6207783 |
| Fulltext version: | Accepted version |
| License (according to publishing contract): | Licence according to publishing contract |
| Departement: | School of Engineering |
| Organisational Unit: | Institute of Computer Science (InIT) |
| Published as part of the ZHAW project: | COSMOS – DevOps for Complex Cyber-physical Systems of Systems |
| Appears in collections: | Publikationen School of Engineering |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2022_diSorbo-etal_Identification-characterization-safety-concerns-UAV-platforms.pdf | Accepted Version | 1.2 MB | Adobe PDF |  View/Open |
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Di Sorbo, A., Zampetti, F., Visaggio, C. A., Di Penta, M., & Panichella, S. (2022). Automated identification and qualitative characterization of safety concerns reported in UAV software platforms. ACM Transactions on Software Engineering and Methodology, 32(3), 67. https://doi.org/10.1145/3564821
Di Sorbo, A. et al. (2022) ‘Automated identification and qualitative characterization of safety concerns reported in UAV software platforms’, ACM Transactions on Software Engineering and Methodology, 32(3), p. 67. Available at: https://doi.org/10.1145/3564821.
A. Di Sorbo, F. Zampetti, C. A. Visaggio, M. Di Penta, and S. Panichella, “Automated identification and qualitative characterization of safety concerns reported in UAV software platforms,” ACM Transactions on Software Engineering and Methodology, vol. 32, no. 3, p. 67, 2022, doi: 10.1145/3564821.
DI SORBO, Andrea, Fiorella ZAMPETTI, Corrado A. VISAGGIO, Massimiliano DI PENTA und Sebastiano PANICHELLA, 2022. Automated identification and qualitative characterization of safety concerns reported in UAV software platforms. ACM Transactions on Software Engineering and Methodology. 2022. Bd. 32, Nr. 3, S. 67. DOI 10.1145/3564821
Di Sorbo, Andrea, Fiorella Zampetti, Corrado A. Visaggio, Massimiliano Di Penta, and Sebastiano Panichella. 2022. “Automated Identification and Qualitative Characterization of Safety Concerns Reported in UAV Software Platforms.” ACM Transactions on Software Engineering and Methodology 32 (3): 67. https://doi.org/10.1145/3564821.
Di Sorbo, Andrea, et al. “Automated Identification and Qualitative Characterization of Safety Concerns Reported in UAV Software Platforms.” ACM Transactions on Software Engineering and Methodology, vol. 32, no. 3, 2022, p. 67, https://doi.org/10.1145/3564821.
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