Im Bereich der IT-Sicherheit konnte ich zusammen mit meinen Kollegen am DLR-Institut für Datenwissenschaften folgende Arbeiten veröffentlichen:
- Sonnekalb, Tim, Thomas S. Heinze, and Patrick Mäder. „Deep security analysis of program code: A systematic literature review.“ Empirical Software Engineering 27.1 (2022): 2.
- Sonnekalb, Tim. „Machine-learning supported vulnerability detection in source code.“ Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. 2019.
- Sonnekalb, T., and S. Lucia. „Smart hot water control with learned human behavior for minimal energy consumption. 2019 IEEE 5th World Forum on Internet of Things (WF-IoT).“ (2019): 572-577.
Schreiber, Andreas, Tim Sonnekalb, and Lynn von Kurnatowski. „Towards visual analytics dashboards for provenance-driven static application security testing.“ 2021 IEEE Symposium on Visualization for Cyber Security (VizSec). IEEE, 2021. - Sonnekalb, Tim, et al. „Towards automated, provenance-driven security audit for git-based repositories: applied to germany’s corona-warn-app: vision paper.“ Proceedings of the 3rd ACM SIGSOFT International Workshop on Software Security from Design to Deployment. 2020.
- Sonnekalb, Tim, et al. „Generalizability of code clone detection on codebert.“ Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering. 2022.
- Schreiber, Andreas, et al. „Provenance-based security audits and its application to COVID-19 contact tracing apps.“ Provenance and Annotation of Data and Processes: 8th and 9th International Provenance and Annotation Workshop, IPAW 2020+ IPAW 2021, Virtual Event, July 19–22, 2021, Proceedings 8. Springer International Publishing, 2021.
- Sonnekalb, Tim, et al. „A Static Analysis Platform for Investigating Security Trends in Repositories.“ 2023 IEEE/ACM 1st International Workshop on Software Vulnerability (SVM). IEEE, 2023.
- Sonnekalb, Tim, Thomas Heinze, and Patrick Mäder. „Erste Überlegungen zur Erklärbarkeitvon Deep-Learning-Modellen für die Analyse von Quellcode.“ WSRE 2020: 22. Workshop Software-Reengineering &-Evolution. Vol. 40. No. 2. Fachbereich Softwaretechnik der Gesellschaft für Informatik, 2020.