Xiaoning DU
TitleCited byYear
DeepStellar: model-based quantitative analysis of stateful deep learning systems
X Du, X Xie, Y Li, L Ma, Y Liu, J Zhao
Proceedings of the 2019 27th ACM Joint Meeting on European Software …, 2019
26*2019
Leopard: Identifying vulnerable code for vulnerability assessment through program metrics
X Du, B Chen, Y Li, J Guo, Y Zhou, Y Liu, Y Jiang
2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE …, 2019
62019
Trace-length independent runtime monitoring of quantitative policies in LTL
X Du, Y Liu, A Tiu
International Symposium on Formal Methods, 231-247, 2015
62015
Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks
Y Zhou, S Liu, J Siow, X Du, Y Liu
https://arxiv.org/abs/1909.03496, 2019
22019
A Quantitative Analysis Framework for Recurrent Neural Network
X Du, X Xie, Y Li, L Ma, Y Liu, J Zhao
2019 34th IEEE/ACM International Conference on Automated Software …, 2019
2019
Who is Real Bob? Adversarial Attacks on Speaker Recognition Systems
G Chen, S Chen, L Fan, X Du, Z Zhao, F Song, Y Liu
arXiv preprint arXiv:1911.01840, 2019
2019
Trace-Length Independent Runtime Monitoring of Quantitative Policies
X Du, A Tiu, K Cheng, Y Liu
IEEE Transactions on Dependable and Secure Computing, 2019
2019
Marvel: a generic, scalable and effective vulnerability detection platform
X Du
2019 IEEE/ACM 41st International Conference on Software Engineering …, 2019
2019
Towards Building a Generic Vulnerability Detection Platform by Combining Scalable Attacking Surface Analysis and Directed Fuzzing
X Du
International Conference on Formal Engineering Methods, 464-468, 2018
2018
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Articles 1–9