Enhancing Large Language Models with Faster Code Preprocessing for Vulnerability Detection
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908355631513600 |
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| author | Gonçalves, José Silva, Miguel Maia, Eva Praça, Isabel |
| author_facet | Gonçalves, José Silva, Miguel Maia, Eva Praça, Isabel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_05600 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing Large Language Models with Faster Code Preprocessing for Vulnerability Detection Gonçalves, José Silva, Miguel Maia, Eva Praça, Isabel Software Engineering Machine Learning 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. |
| title | Enhancing Large Language Models with Faster Code Preprocessing for Vulnerability Detection |
| topic | Software Engineering Machine Learning |
| url | https://arxiv.org/abs/2505.05600 |