Harnessing the Efficiency of Reformers to Detect Software Vulnerabilities

Detecting software vulnerabilities is a critical task in ensuring the security of software systems. Recent advances in deep learning (DL) have shown promise in improving the accuracy of vulnerability detection. In this work, we proposed an approach for vulnerability detection using the Reformer language model. We compared our approach with previous works on three datasets: SARD, D2A, and Devign. Our experiments showed that our approach outperforms the state-of-the-art methods on all three datasets, achieving a precision of 0.95, recall of 0.91, and F1-score of 0.93 on average. Our approach is computationally efficient, making it suitable for large-scale vulnerability detection tasks. We also provide an in-depth analysis of the results and discuss the implications of our findings. Our work contributes to the growing body of literature on DL based vulnerability detection and demonstrates the potential of the Reformer model in this domain.