Vudenc: Vulnerability Detection with Deep Learning on a Natural Codebase for Python - Summary

dc.contributor.author	Wartschinski, Laura
dc.contributor.author	Noller, Yannic
dc.contributor.author	Vogel, Thomas
dc.contributor.author	Kehrer, Timo
dc.contributor.author	Grunske, Lars
dc.contributor.editor	Engels, Gregor
dc.contributor.editor	Hebig, Regina
dc.contributor.editor	Tichy, Matthias
dc.date.accessioned	2023-01-18T13:38:52Z
dc.date.available	2023-01-18T13:38:52Z
dc.date.issued	2023
dc.description.abstract	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.	en
dc.identifier.isbn	978-3-88579-726-5
dc.identifier.pissn	1617-5468
dc.identifier.uri	https://dl.gi.de/handle/20.500.12116/40117
dc.language.iso	en
dc.publisher	Gesellschaft für Informatik e.V.
dc.relation.ispartof	Software Engineering 2023
dc.relation.ispartofseries	Lecture Notes in Informatics (LNI) - Proceedings, Volume P-332
dc.subject	Vulnerability detection
dc.subject	Python
dc.subject	Deep learning
dc.title	Vudenc: Vulnerability Detection with Deep Learning on a Natural Codebase for Python - Summary	en
dc.type	Text/Conference Paper
gi.citation.endPage	126
gi.citation.publisherPlace	Bonn
gi.citation.startPage	125
gi.conference.date	20.–24. Februar 2023
gi.conference.location	Paderborn
gi.conference.sessiontitle	Wissenschaftliches Hauptprogramm

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