Enhancing Vulnerability Detection Precision through Ensemble Learning with Large Language Models

Abstract

This study investigates the use of ensemble learning with Large Language Models (LLMs) to improve the accuracy of software vulnerability prediction, following a structured experimental approach to assess whether combining multiple models can enhance performance. Three baseline models, CodeBERT, GraphCodeBERT, and CodeT5, were trained and assessed on the Devign dataset, which provides a large collection of labeled source code snippets. Their outputs were then integrated using three ensemble techniques: Majority Voting, Weighted Voting, and Stacking. Precision, recall, and F1-score metrics were used to gauge performance. Ensemble approaches outperformed all standalone models. In particular, Majority Voting increased precision from 0.601 (CodeBERT) to 0.690, representing a 14.81% improvement. Keeping in view the detection accuracy, this study focused on reducing the false positives. The results show that the ensemble techniques are a practical approach to boost the precision of LLMs in the detection of vulnerabilities. Ensemble learning can address the challenges faced by standalone models by reducing false positives and improving the overall trade-off between accuracy and reliability. The study suggests that ensemble methods offer great potential in the advancement of software security analysis.