Year: 2025
Type: conference-paper
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.
Author: Leonardo Centellas-Claros
Institution: Pontificia Universidad Católica de Chile
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4412704034