TY - JOUR
T1 - Machine Learning for Source Code Vulnerability Detection
T2 - What Works and What Isn't There Yet
AU - Marjanov, Tina
AU - Pashchenko, Ivan
AU - Massacci, Fabio
N1 - Publisher Copyright:
© 2003-2012 IEEE.
PY - 2022/10
Y1 - 2022/10
N2 - We review machine learning approaches for detecting (and correcting) vulnerabilities in source code, finding that the biggest challenges ahead involve agreeing to a benchmark, increasing language and error type coverage, and using pipelines that do not flatten the code's structure.
AB - We review machine learning approaches for detecting (and correcting) vulnerabilities in source code, finding that the biggest challenges ahead involve agreeing to a benchmark, increasing language and error type coverage, and using pipelines that do not flatten the code's structure.
UR - http://www.scopus.com/inward/record.url?scp=85136889148&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85136889148&partnerID=8YFLogxK
U2 - 10.1109/MSEC.2022.3176058
DO - 10.1109/MSEC.2022.3176058
M3 - Article
AN - SCOPUS:85136889148
SN - 1540-7993
VL - 20
SP - 60
EP - 76
JO - IEEE Security and Privacy
JF - IEEE Security and Privacy
IS - 5
ER -