Learning from what we know: How to perform vulnerability prediction using noisy historical data

Reference : Learning from what we know: How to perform vulnerability prediction using noisy histo...


	Document type :	Scientific journals : Article
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Security, Reliability and Trust
	To cite this reference:	http://hdl.handle.net/10993/45529

Title :	Learning from what we know: How to perform vulnerability prediction using noisy historical data
Language :	English
Author, co-author :	Garg, Aayush [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)]
	Degiovanni, Renzo Gaston [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SerVal]
	Jimenez, Matthieu [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)]
	Cordy, Maxime [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SerVal]
	Papadakis, Mike [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)]
	Le Traon, Yves [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SerVal >]
Publication date :	20-Sep-2022
Journal title :	Empirical Software Engineering
Publisher :	Springer
Peer reviewed :	Yes
ISSN :	1382-3256
e-ISSN :	1573-7616
Country :	Netherlands
Permalink :	http://hdl.handle.net/10993/45529
Other URL :	https://github.com/garghub/TROVON

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Open access	s10664-022-10197-4.pdf		Publisher postprint	1.87 MB	View/Open

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