Xiongfei Wu

Orcid: 0009-0009-8457-0839

According to our database1, Xiongfei Wu authored at least 13 papers between 2020 and 2026.

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

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

On csauthors.net:

Bibliography

2026
QSPE: Enumerating Skeletal Quantum Programs for Quantum Library Testing.
CoRR, February, 2026

2025
The Tower of Babel Revisited: Multilingual Jailbreak Prompts on Closed-Source Large Language Models.
CoRR, May, 2025

Foundation Models for Autonomous Driving System: An Initial Roadmap.
CoRR, April, 2025

Improving Adversarial Training for Two-player Competitive Games via Episodic Reward Engineering.
Trans. Mach. Learn. Res., 2025

Is Measurement Enough? Rethinking Output Validation in Quantum Program Testing.
Proceedings of the 40th IEEE/ACM International Conference on Automated Software Engineering, 2025

Demystifying the Evolution of Neural Networks with BOM Analysis: Insights from a Large-Scale Study of 55,997 GitHub Repositories.
Proceedings of the 40th IEEE/ACM International Conference on Automated Software Engineering, 2025

2024
PrivAuditor: Benchmarking Data Protection Vulnerabilities in LLM Adaptation Techniques.
Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

2023
An Empirical Study of Bugs in Quantum Machine Learning Frameworks.
Proceedings of the IEEE International Conference on Quantum Software, 2023

Generative Model-Based Testing on Decision-Making Policies.
Proceedings of the 38th IEEE/ACM International Conference on Automated Software Engineering, 2023

Widget Detection-based Testing for Industrial Mobile Games.
Proceedings of the 45th IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice, 2023

QChecker: Detecting Bugs in Quantum Programs via Static Analysis.
Proceedings of the 4th IEEE/ACM International Workshop on Quantum Software Engineering, 2023

2022
On the usage and development of deep learning compilers: an empirical study on TVM.
Empir. Softw. Eng., 2022

2020
How are Deep Learning Models Similar?: An Empirical Study on Clone Analysis of Deep Learning Software.
Proceedings of the ICPC '20: 28th International Conference on Program Comprehension, 2020


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