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
Peer Reviewed verified by ORBi
 

Files


Full Text
s10664-022-10197-4.pdf
Publisher postprint (1.92 MB)
Download

All documents in ORBilu are protected by a user license.

Send to



Details



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

Statistics


Number of views
240 (47 by Unilu)
Number of downloads
53 (5 by Unilu)

Scopus citations®
 
8
Scopus citations®
without self-citations
4
OpenCitations
 
0
WoS citations
 
7

Bibliography


Similar publications



Contact ORBilu