Automated Vulnerability-Focused Code Reviews

Code review is an important practice in software development, aimed at ensuring code quality and mitigating potential issues. Although effective, manual code reviews are time-consuming. In response, the research community has explored automating this process using deep learning. However, existing models, designed to address a broad spectrum of code review types, often fail to account for the unique aspects of reviews specifically related to vulnerabilities. One reason is the underrepresentation of such reviews in training datasets, which impacts the accuracy of these models. Consequently, there is limited knowledge on the specific characteristics of vulnerability-focused code reviews and the performance of current models in this specialized context.

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Published
2025-06-23
DOI
https://doi.org/10.1145/3696630.3731467
Primary Topic
Software Reliability and Analysis Research
Type
article
Field-Weighted Citation Impact
0.00

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article

Automated Vulnerability-Focused Code Reviews

Leonardo Centellas-Claros
Software Reliability and Analysis Research
article

Automated Vulnerability-Focused Code Reviews

Leonardo Centellas-Claros
article en

Abstract

Code review is an important practice in software development, aimed at ensuring code quality and mitigating potential issues. Although effective, manual code reviews are time-consuming. In response, the research community has explored automating this process using deep learning. However, existing models, designed to address a broad spectrum of code review types, often fail to account for the unique aspects of reviews specifically related to vulnerabilities. One reason is the underrepresentation of such reviews in training datasets, which impacts the accuracy of these models. Consequently, there is limited knowledge on the specific characteristics of vulnerability-focused code reviews and the performance of current models in this specialized context.

Pontificia Universidad Católica de Chile (CL)
Agenția Națională pentru Cercetare și Dezvoltare, Agencia Nacional de Investigación y Desarrollo, CHIST-ERA
Openalex Percentile: Top 81%
Software Reliability and Analysis Research
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