<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Enhancing software quality and security hinges on the effective identification of vulnerabilities in source code. This paper presents a novel approach that combines pattern recognition training with cloze-style examination techniques in a semi-supervised learning framework. Our methodology involves training a language model using the SARD and Devign datasets, which contain numerous examples of vulnerable code. During training, specific code sections are deliberately obscured, challenging the model to predict the hidden tokens. Through rigorous empirical testing, we demonstrate the effectiveness of our approach in accurately identifying code vulnerabilities. Our results highlight the significant advantages of employing pattern recognition training alongside cloze-style questioning, leading to improved accuracy in detecting vulnerabilities in source code.</p>
</abstract>

Enhancing software quality and security hinges on the effective identification of vulnerabilities in source code. This paper presents a novel approach that combines pattern recognition training with cloze-style examination techniques in a semi-supervised learning framework. Our methodology involves training a language model using the SARD and Devign datasets, which contain numerous examples of vulnerable code. During training, specific code sections are deliberately obscured, challenging the model to predict the hidden tokens. Through rigorous empirical testing, we demonstrate the effectiveness of our approach in accurately identifying code vulnerabilities. Our results highlight the significant advantages of employing pattern recognition training alongside cloze-style questioning, leading to improved accuracy in detecting vulnerabilities in source code.

Codeguard: Utilizing Advanced Pattern Recognition in Language Models for Software Vulnerability Analysis

Land Forces Academy Review

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Enhancing software quality and security hinges on the effective identification of vulnerabilities in source code. This paper presents a novel approach that...

Codeguard: Utilizing Advanced Pattern Recognition in Language Models for Software Vulnerability Analysis

Publié en ligne: 28 févr. 2024

Pages: 108 - 118

DOI: https://doi.org/10.2478/raft-2024-0011

Mots clés
language models, software vulnerabilities, vulnerability detection, cloze-style questions, pattern-exploiting training, RoBERTa

© 2024 Rebet Jones et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Codeguard: Utilizing Advanced Pattern Recognition in Language Models for Software Vulnerability Analysis

Publié en ligne: 28 févr. 2024

Pages: 108 - 118

DOI: https://doi.org/10.2478/raft-2024-0011

Mots cléslanguage models, software vulnerabilities, vulnerability detection, cloze-style questions, pattern-exploiting training, RoBERTa

© 2024 Rebet Jones et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
language models, software vulnerabilities, vulnerability detection, cloze-style questions, pattern-exploiting training, RoBERTa