| 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.identifier.isbn | 978-3-88579-726-5 | |
| dc.identifier.issn | 1617-5468 | |
| dc.identifier.uri | http://dl.gi.de/handle/20.500.12116/40117 | |
| 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.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 | |
| dc.pubPlace | Bonn | |
| mci.reference.pages | 125-126 | |
| mci.conference.sessiontitle | Wissenschaftliches Hauptprogramm | |
| mci.conference.location | Paderborn | |
| mci.conference.date | 20.–24. Februar 2023 | |
