AI-Based Software Vulnerability Detection: A Systematic Literature Review
Fuente:
arXiv
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866913890027175936 |
|---|---|
| author | Shimmi, Samiha Okhravi, Hamed Rahimi, Mona |
| author_facet | Shimmi, Samiha Okhravi, Hamed Rahimi, Mona |
| contents | Software vulnerabilities in source code pose serious cybersecurity risks, prompting a shift from traditional detection methods (e.g., static analysis, rule-based matching) to AI-driven approaches. This study presents a systematic review of software vulnerability detection (SVD) research from 2018 to 2023, offering a comprehensive taxonomy of techniques, feature representations, and embedding methods. Our analysis reveals that 91% of studies use AI-based methods, with graph-based models being the most prevalent. We identify key limitations, including dataset quality, reproducibility, and interpretability, and highlight emerging opportunities in underexplored techniques such as federated learning and quantum neural networks, providing a roadmap for future research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_10280 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI-Based Software Vulnerability Detection: A Systematic Literature Review Shimmi, Samiha Okhravi, Hamed Rahimi, Mona Software Engineering Cryptography and Security Software vulnerabilities in source code pose serious cybersecurity risks, prompting a shift from traditional detection methods (e.g., static analysis, rule-based matching) to AI-driven approaches. This study presents a systematic review of software vulnerability detection (SVD) research from 2018 to 2023, offering a comprehensive taxonomy of techniques, feature representations, and embedding methods. Our analysis reveals that 91% of studies use AI-based methods, with graph-based models being the most prevalent. We identify key limitations, including dataset quality, reproducibility, and interpretability, and highlight emerging opportunities in underexplored techniques such as federated learning and quantum neural networks, providing a roadmap for future research. |
| title | AI-Based Software Vulnerability Detection: A Systematic Literature Review |
| topic | Software Engineering Cryptography and Security |
| url | https://arxiv.org/abs/2506.10280 |