Enhancing Large Language Models with Faster Code Preprocessing for Vulnerability Detection

TL;DR

This paper introduces SCoPE2, an improved code preprocessing tool that significantly speeds up processing and enhances vulnerability detection accuracy in large language models by standardizing code representation.

Contribution

SCoPE2 extends the existing SCoPE framework with better performance, reducing processing time by 97.3% and improving LLM vulnerability detection accuracy.

Findings

97.3% reduction in processing time

Improved F1-score for vulnerability detection

Enhanced code standardization benefits LLM performance

Abstract

The application of Artificial Intelligence has become a powerful approach to detecting software vulnerabilities. However, effective vulnerability detection relies on accurately capturing the semantic structure of code and its contextual relationships. Given that the same functionality can be implemented in various forms, a preprocessing tool that standardizes code representation is important. This tool must be efficient, adaptable across programming languages, and capable of supporting new transformations. To address this challenge, we build on the existing SCoPE framework and introduce SCoPE2, an enhanced version with improved performance. We compare both versions in terms of processing time and memory usage and evaluate their impact on a Large Language Model (LLM) for vulnerability detection. Our results show a 97.3\% reduction in processing time with SCoPE2, along with an improved F1-score for the LLM, solely due to the refined preprocessing approach.