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Empirical Software Engineering

Erschienen in:

01.12.2022

SPVF: security property assisted vulnerability fixing via attention-based models

verfasst von: Zhou Zhou, Lili Bo, Xiaoxue Wu, Xiaobing Sun, Tao Zhang, Bin Li, Jiale Zhang, Sicong Cao

Erschienen in: Empirical Software Engineering | Ausgabe 7/2022

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Abstract

The past few years have witnessed the wide application of machine learning models to fix vulnerabilities automatically. However, existing approaches cannot capture the characteristics of vulnerabilities that are helpful to improve the effectiveness of automated vulnerability fixing. In this paper, we propose a novel approach for automatically fixing vulnerabilities, called SPVF. SPVF captures the security property from the descriptive information about the vulnerability. SPVF is based on the attention mechanism and uses the abstract syntax tree as well as the security properties, integrating them using the pointer generator. The experimental results on two public datasets show that SPVF outperforms the state-of-the-art approaches by 13% for C/C++ and 47% for Python. And SPVF is capable of successfully fixing 153 C/C++ vulnerabilities and 276 Python vulnerabilities.

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https://zenodo.org/record/3559203#.XeRoytVG2Hs

https://zenodo.org/record/6324846

Note that a small number of vulnerabilities cannot be classified into the classifications listed due to the absence of CWE id information in the dataset.

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Titel: SPVF: security property assisted vulnerability fixing via attention-based models
verfasst von: Zhou Zhou
Lili Bo
Xiaoxue Wu
Xiaobing Sun
Tao Zhang
Bin Li
Jiale Zhang
Sicong Cao
Publikationsdatum: 01.12.2022
Verlag: Springer US
Erschienen in: Empirical Software Engineering / Ausgabe 7/2022
Print ISSN: 1382-3256
Elektronische ISSN: 1573-7616
DOI: https://doi.org/10.1007/s10664-022-10216-4

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