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.
Machine Learning for Source Code Vulnerability Detection: What Works and What Isn't There Yet / Marjanov, T.; Pashchenko, I.; Massacci, F.. - In: IEEE SECURITY & PRIVACY. - ISSN 1540-7993. - 20:5(2022), pp. 60-76. [10.1109/MSEC.2022.3176058]
Machine Learning for Source Code Vulnerability Detection: What Works and What Isn't There Yet
Pashchenko I.;Massacci F.
Ultimo
2022-01-01
Abstract
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.File in questo prodotto:
Non ci sono file associati a questo prodotto.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione
