Xiaoning Du

Orcid: 0000-0003-3728-9541

Affiliations:
  • Monash University, Clayton, VIC, Australia
  • Nanyang Technological University, Singapore (PhD 2020)


According to our database1, Xiaoning Du authored at least 28 papers between 2015 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Online presence:

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Bibliography

2024
A Proactive and Dual Prevention Mechanism against Illegal Song Covers empowered by Singing Voice Conversion.
CoRR, 2024

Are Latent Vulnerabilities Hidden Gems for Software Vulnerability Prediction? An Empirical Study.
CoRR, 2024

When Neural Code Completion Models Size up the Situation: Attaining Cheaper and Faster Completion through Dynamic Model Inference.
CoRR, 2024

2023
Pop Quiz! Do Pre-trained Code Models Possess Knowledge of Correct API Names?
CoRR, 2023

Data Augmentation Approaches for Source Code Models: A Survey.
CoRR, 2023

FuzzJIT: Oracle-Enhanced Fuzzing for JavaScript Engine JIT Compiler.
Proceedings of the 32nd USENIX Security Symposium, 2023

DistXplore: Distribution-Guided Testing for Evaluating and Enhancing Deep Learning Systems.
Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2023

CodeMark: Imperceptible Watermarking for Code Datasets against Neural Code Completion Models.
Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2023

Don't Complete It! Preventing Unhelpful Code Completion for Productive and Sustainable Neural Code Completion Systems.
Proceedings of the 45th IEEE/ACM International Conference on Software Engineering: ICSE 2023 Companion Proceedings, 2023

2022
Vulnerability Analysis, Robustness Verification, and Mitigation Strategy for Machine Learning-Based Power System Stability Assessment Model Under Adversarial Examples.
IEEE Trans. Smart Grid, 2022

Learning to Prevent Profitless Neural Code Completion.
CoRR, 2022

CoProtector: Protect Open-Source Code against Unauthorized Training Usage with Data Poisoning.
Proceedings of the WWW '22: The ACM Web Conference 2022, Virtual Event, Lyon, France, April 25, 2022

Characterizing Python Method Evolution with PyMevol: An Essential Step Towards Enabling Reliable Software Systems.
Proceedings of the IEEE International Symposium on Software Reliability Engineering Workshops, 2022

On the Importance of Building High-quality Training Datasets for Neural Code Search.
Proceedings of the 44th IEEE/ACM 44th International Conference on Software Engineering, 2022

2021
Trace-Length Independent Runtime Monitoring of Quantitative Policies.
IEEE Trans. Dependable Secur. Comput., 2021

Who is Real Bob? Adversarial Attacks on Speaker Recognition Systems.
Proceedings of the 42nd IEEE Symposium on Security and Privacy, 2021

Decision-Guided Weighted Automata Extraction from Recurrent Neural Networks.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020
Towards secure and robust stateful deep learning systems with model-based analysis
PhD thesis, 2020

Marble: Model-based Robustness Analysis of Stateful Deep Learning Systems.
Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering, 2020

Towards characterizing adversarial defects of deep learning software from the lens of uncertainty.
Proceedings of the ICSE '20: 42nd International Conference on Software Engineering, Seoul, South Korea, 27 June, 2020

2019
DeepStellar: model-based quantitative analysis of stateful deep learning systems.
Proceedings of the ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2019

Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

A Quantitative Analysis Framework for Recurrent Neural Network.
Proceedings of the 34th IEEE/ACM International Conference on Automated Software Engineering, 2019

Marvel: a generic, scalable and effective vulnerability detection platform.
Proceedings of the 41st International Conference on Software Engineering: Companion Proceedings, 2019

Leopard: identifying vulnerable code for vulnerability assessment through program metrics.
Proceedings of the 41st International Conference on Software Engineering, 2019

2018
DeepCruiser: Automated Guided Testing for Stateful Deep Learning Systems.
CoRR, 2018

Towards Building a Generic Vulnerability Detection Platform by Combining Scalable Attacking Surface Analysis and Directed Fuzzing.
Proceedings of the Formal Methods and Software Engineering, 2018

2015
Trace-Length Independent Runtime Monitoring of Quantitative Policies in LTL.
Proceedings of the FM 2015: Formal Methods, 2015


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