Question-Answer Methodology for Vulnerable Source Code Review via Prototype-Based Model-Agnostic Meta-Learning

doi:10.3390/fi17010033

Question-Answer Methodology for Vulnerable Source Code Review via Prototype-Based Model-Agnostic Meta-Learning

2025-01-14

0

OA

AI

P

Pablo Corona-Fraga

A

Aldo Hernandez-Suarez

G

Gabriel Sanchez-Perez *

José Portillo-Portillo

J

Jesús Olivares-Mercado

L

Luis Javier García Villalba *

DOI：10.3390/fi17010033

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Abstract

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In cybersecurity, identifying and addressing vulnerabilities in source code is essential for maintaining secure IT environments. Traditional static and dynamic analysis techniques, although widely used, often exhibit high false-positive rates, elevated costs, and limited interpretability. Machine Learning (ML)-based approaches aim to overcome these limitations but encounter challenges related to scalability and adaptability due to their reliance on large labeled datasets and their limited alignment with the requirements of secure development teams. These factors hinder their ability to adapt to rapidly evolving software environments. This study proposes an approach that integrates Prototype-Based Model-Agnostic Meta-Learning(Proto-MAML) with a Question-Answer (QA) framework that leverages the Bidirectional Encoder Representations from Transformers (BERT) model. By employing Few-Shot Learning (FSL), Proto-MAML identifies and mitigates vulnerabilities with minimal data requirements, aligning with the principles of the Secure Development Lifecycle (SDLC) and Development, Security, and Operations (DevSecOps). The QA framework allows developers to query vulnerabilities and receive precise, actionable insights, enhancing its applicability in dynamic environments that require frequent updates and real-time analysis. The model outputs are interpretable, promoting greater transparency in code review processes and enabling efficient resolution of emerging vulnerabilities. Proto-MAML demonstrates strong performance across multiple programming languages, achieving an average precision of 98.49%, recall of 98.54%, F1-score of 98.78%, and exact match rate of 98.78% in PHP, Java, C, and C++.

Keywords:

question-answer methodology

vulnerable source code review

prototype-based learning

model-agnostic meta-learning

Proto-MAML

code vulnerability detection

software security

few-shot learning

source code analysis

meta-learning techniques

automated code review

cybersecurity

knowledge transfer

code reconstruction

Question-Answer Methodology for Vulnerable Source Code Review via Prototype-Based Model-Agnostic Meta-Learning

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