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Vulnerability prediction from source code using Machine Learning

In this study, we examine how to predict software vulnerabilities from source code by employing ML prior to their release. To this end, we develop a source code representation method that enables us to perform intelligent analysis on the Abstract Syntax Tree (AST) form of source code and then investigate whether ML can distinguish vulnerable and nonvulnerable code fragments.
Research paper

We show the effectiveness of our proposed method for vulnerability prediction from source code by carrying out exhaustive and realistic experiments under different regimes in comparison with state-of-art methods.

Full abstract in IEEE Xplore, DOI:10.1109/ACCESS.2020.3016774

Authors: 

Zeki Bilgin, Mehmet Akif Ersoy, Elif Üstündağ Soykan, Emrah Tomur, Pınar Çomak, Leyli Karaçay – Ericsson Research

 

Published in IEEE Access Journal, August 14, 2020.

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