Tim Sonnekalb
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Machine-learning supported vulnerability detection in source code
T Sonnekalb
ESEC/FSE 2019, 1180-1183, 2019
Deep security analysis of program code: A systematic literature review
T Sonnekalb, TS Heinze, P Mäder
Empirical Software Engineering 27 (1), 2, 2022
Towards automated, provenance-driven security audit for git-based repositories: applied to germany's corona-warn-app: vision paper
T Sonnekalb, TS Heinze, L Kurnatowski, A Schreiber, ...
Proceedings of the 3rd ACM SIGSOFT International Workshop on Software …, 2020
Smart hot water control with learned human behavior for minimal energy consumption
T Sonnekalb, S Lucia
2019 IEEE 5th World Forum on Internet of Things (WF-IoT), 572-577, 2019
Towards visual analytics dashboards for provenance-driven static application security testing
A Schreiber, T Sonnekalb, L von Kurnatowski
2021 IEEE Symposium on Visualization for Cyber Security (VizSec), 42-46, 2021
Provenance-based security audits and its application to COVID-19 contact tracing apps
A Schreiber, T Sonnekalb, TS Heinze, L von Kurnatowski, ...
Provenance and Annotation of Data and Processes: 8th and 9th International …, 2021
ROMEO: A Binary Vulnerability Detection Dataset for Exploring Juliet through the Lens of Assembly Language
CA Brust, T Sonnekalb, B Gruner
Computers & Security, 103165, 2023
Generalizability of Code Clone Detection on CodeBERT
T Sonnekalb, B Gruner, CA Brust, P Mäder
arXiv preprint arXiv:2208.12588, 2022
Cross-Domain Evaluation of a Deep Learning-Based Type Inference System
B Gruner, T Sonnekalb, TS Heinze, CA Brust
arXiv preprint arXiv:2208.09189, 2022
Machine Learning Applications in Secure Software Engineering
BE Bouhlal, B Gruner, T Sonnekalb, CA Brust
ROMEO: Exploring Juliet through the Lens of Assembly Language
CA Brust, B Gruner, T Sonnekalb
arXiv preprint arXiv:2112.06623, 2021
Automated, Provenance-Driven Security Audit for git-Based Repositories
M Stoffers, L Kurnatowski, T Sonnekalb, A Schreiber
Software Product Assurance Workshop, 2021
Security Audit for Contact Tracing Apps applied to the Corona-Warn-App
T Sonnekalb, L Kurnatowski
WAW SE VII, 2020
Erste Überlegungen zur Erklärbarkeit von Deep-Learning-Modellen für die Analyse von Quellcode
T Sonnekalb, TS Heinze, P Mäder
WSRE 2020: 22. Workshop Software-Reengineering & -Evolution 40 (2), 2, 2020
von Kurnatowski, Lynn 42
SJ Yang, K Berlin, G Blasilli, F Böhm, S Bonomi, E Cakmak, G Denker, ...
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