33 citations · 72 across the 4 of their papers we have counts for
6 papers · 1 filter
CodeMark: Imperceptible Watermarking for Code Datasets against Neural Code Completion Models
Zhensu Sun, Xiaoning Du, Fu Song +1
Code datasets are of immense value for training neural-network-based code completion models, where companies or organizations have made substantial investments to establish and pro…
On the Importance of Building High-quality Training Datasets for Neural Code Search
Zhensu Sun, Li Li, Yan Liu +1
The performance of neural code search is significantly influenced by the quality of the training data from which the neural models are derived. A large corpus of high-quality query…
Towards Characterizing Adversarial Defects of Deep Learning Software from the Lens of Uncertainty
Xiyue Zhang, Xiaofei Xie, Lei Ma +5
Over the past decade, deep learning (DL) has been successfully applied to many industrial domain-specific tasks. However, the current state-of-the-art DL software still suffers fro…
Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks
Yaqin Zhou, Shangqing Liu, Jingkai Siow +2
Vulnerability identification is crucial to protect the software systems from attacks for cyber security. It is especially important to localize the vulnerable functions among the s…
LEOPARD: Identifying Vulnerable Code for Vulnerability Assessment through Program Metrics
Xiaoning Du, Bihuan Chen, Yuekang Li +4
Identifying potentially vulnerable locations in a code base is critical as a pre-step for effective vulnerability assessment; i.e., it can greatly help security experts put their t…
DeepCruiser: Automated Guided Testing for Stateful Deep Learning Systems
Xiaoning Du, Xiaofei Xie, Yi Li +3
Deep learning (DL) defines a data-driven programming paradigm that automatically composes the system decision logic from the training data. In company with the data explosion and h…