Vudenc: Vulnerability Detection with Deep Learning on a Natural Codebase for Python - Summary
Autor(en):
Zusammenfassung
In this extended abstract, we summarize our work on Vudenc published in the journal Information and Software Technology (IST) in 2022 [Wa22]. Vudenc uses deep learning to learn features of vulnerable code from a real-world Python codebase and a network of long-short-term memory cells (LSTM) is then used to detect vulnerabilities in code at a fine-grained level.
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- BibTeX
Wartschinski, L., Noller, Y., Vogel, T., Kehrer, T. & Grunske, L.,
(2023).
Vudenc: Vulnerability Detection with Deep Learning on a Natural Codebase for Python - Summary.
In:
Engels, G., Hebig, R. & Tichy, M.
(Hrsg.),
Software Engineering 2023.
Bonn:
Gesellschaft für Informatik e.V..
(S. 125-126).
@inproceedings{mci/Wartschinski2023,
author = {Wartschinski, Laura AND Noller, Yannic AND Vogel, Thomas AND Kehrer, Timo AND Grunske, Lars},
title = {Vudenc: Vulnerability Detection with Deep Learning on a Natural Codebase for Python - Summary},
booktitle = {Software Engineering 2023},
year = {2023},
editor = {Engels, Gregor AND Hebig, Regina AND Tichy, Matthias} ,
pages = { 125-126 },
publisher = {Gesellschaft für Informatik e.V.},
address = {Bonn}
}
author = {Wartschinski, Laura AND Noller, Yannic AND Vogel, Thomas AND Kehrer, Timo AND Grunske, Lars},
title = {Vudenc: Vulnerability Detection with Deep Learning on a Natural Codebase for Python - Summary},
booktitle = {Software Engineering 2023},
year = {2023},
editor = {Engels, Gregor AND Hebig, Regina AND Tichy, Matthias} ,
pages = { 125-126 },
publisher = {Gesellschaft für Informatik e.V.},
address = {Bonn}
}
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Mehr Information
ISBN: 978-3-88579-726-5
ISSN: 1617-5468
Datum: 2023
Sprache:
(en)
(en)
Typ: Text/Conference Paper

