Article (Scientific journals)
Learning from what we know: How to perform vulnerability prediction using noisy historical data
Garg, Aayush; Degiovanni, Renzo Gaston; Jimenez, Matthieu et al.
2022In Empirical Software Engineering
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Disciplines :
Computer science
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
External co-authors :
no
Language :
English
Title :
Learning from what we know: How to perform vulnerability prediction using noisy historical data
Publication date :
20 September 2022
Journal title :
Empirical Software Engineering
ISSN :
1573-7616
Publisher :
Springer, Netherlands
Peer reviewed :
Peer Reviewed verified by ORBi
Focus Area :
Security, Reliability and Trust
Available on ORBilu :
since 15 January 2021

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