Vulnerable Function

Vulnerable function identification focuses on automatically detecting security flaws within software functions, aiming to improve software security and reduce the burden on human code reviewers. Current research employs various machine learning approaches, including graph neural networks, ensemble learning methods, and deep learning models trained on large datasets of vulnerable and non-vulnerable code, often leveraging code properties like function names and program graphs. However, challenges remain in accurately identifying vulnerabilities, particularly due to issues like spurious correlations in datasets and the difficulty of distinguishing between vulnerable and patched code. Addressing these limitations is crucial for advancing the field and enabling more effective automated vulnerability detection tools.