3 papers
cs.SE2025
A Study on Mixup-Inspired Augmentation Methods for Software Vulnerability Detection
Seyed Shayan Daneshvar, Da Tan, Shaowei Wang +1
Various deep learning (DL) methods have recently been utilized to detect software vulnerabilities. Real-world software vulnerability datasets are rare and hard to acquire, as there…
cs.CV2024
GUI Element Detection Using SOTA YOLO Deep Learning Models
Seyed Shayan Daneshvar, Shaowei Wang
Detection of Graphical User Interface (GUI) elements is a crucial task for automatic code generation from images and sketches, GUI testing, and GUI search. Recent studies have leve…
cs.SE2024
VulScribeR: Exploring RAG-based Vulnerability Augmentation with LLMs
Seyed Shayan Daneshvar, Yu Nong, Xu Yang +2
Detecting vulnerabilities is vital for software security, yet deep learning-based vulnerability detectors (DLVD) face a data shortage, which limits their effectiveness. Data augmen…