Qiang Hu   Research Associate Building 12 room 519 Asano Campus The University of Tokyo Email: qianghu0515[at]gmail[dot]com

Biography

I am a research associate at the University of Tokyo, working with Prof. Lei Ma. I received my Ph.D. degree from the University of Luxembourg, advised by Prof. Yves Le Traon. Before that, I received my Master degree from Kyushu University, advised by Prof. Jianjun Zhao, and my B.S. degree from the University of Electronic Science and Technology of China.

My research interests span the areas of software engineering and deep learning, including deep learning testing, AIOps, and AI4SE.
Our recent survey about label-efficient testing of DL can be found at Survey.

Papers

* corresponding author. Full paper list can be found at [Google Scholar].

1. Towards Exploring the Limitations of Test Selection Techniques on Graph Neural Networks: An Empirical Study
   Xueqi Dang, Yinghua Li, Wei Ma, Yuejun Guo, Qiang Hu, Mike Papadakis, Maxime Cordy, and Yves Le Traon.
   In Empirical Software Engineering (EMSE) 2024
   
2. Outside the Comfort Zone: Analysing LLM Capabilities in Software Vulnerability Detection
   Yuejun Guo, Constantinos Patsakis, Qiang Hu, Qiang Tang and Fran Casino.
   In 29th European Symposium on Research in Computer Security (ESORICS) 2024.
   
3. On the Effectiveness of Hybrid Pooling in Mixup-Based Graph Learning for Language Processing
   Zeming Dong, Qiang Hu*, Yuejun Guo, Zhenya Zhang, Maxime Cordy, Mike Papadakis, Yves Le Traon, and Jianjun Zhao.
   In Journal of Systems and Software (JSS) 2024
   
4. Active Code Learning: Benchmarking Sample-Efficient Training of Code Models
   Qiang Hu, Yuejun Guo, Xiaofei Xie, Maxime Cordy, Lei Ma, Mike Papadakis, and Yves Le Traon
   In IEEE Transactions on Software Engineering (TSE) 2024.
   
5. Test Optimization in DNN Testing: A Survey
   Qiang Hu, Yuejun Guo, Xiaofei Xie, Maxime Cordy, Lei Ma, Mike Papadakis, and Yves Le Traon
   In ACM Transactions on Software Engineering and Methodology (TOSEM) 2024
   
6. LaF: Labeling-Free Model Selection for Automated Deep Neural Network Reusing
   Qiang Hu, Yuejun Guo, Maxime Cordy, Xiaofei Xie, Mike Papadakis, and Yves Le Traon
   In ACM Transactions on Software Engineering and Methodology (TOSEM) 2023
   
7. Aries: Efficient Testing of Deep Neural Networks via Labeling-Free Accuracy Estimation
   Qiang Hu, Yuejun Guo, Xiaofei Xie, Maxime Cordy, Lei Ma, Mike Papadakis, and Yves Le Traon
   In Proc. 45nd International Conference on Software Engineering (ICSE 2023)
   
8. Towards Understanding Model Quantization for Reliable Deep Neural Network Deployment
   Qiang Hu, Yuejun Guo, Xiaofei Xie, Maxime Cordy, Wei Ma, Mike Papadakis, and Yves Le Traon
   In 2nd International Conference on AI Engineering Software Engineering for AI (CAIN) 2023
   
9. An Empirical Study on Data Distribution-Aware Test Selection for Deep Learning Enhancement
   Qiang Hu, Yuejun Guo, Maxime Cordy, Xiaofei Xie, Lei Ma, Mike Papadakis and Yves Le Traon
   In ACM Transactions on Software Engineering and Methodology (TOSEM) 2022
   
10. Towards Exploring the Limitations of Active learning: An Empirical Study
    Qiang Hu, Yuejun Guo, Maxime Cordy, Xiaofei Xie, Wei Ma, Mike Papadakis and Yves Le Traon
    In Proc. 36th IEEE/ACM Conference on Automated Software Engineering (ASE 2021)
    
11. MUTEN: Mutant-Based Ensembles for Boosting Gradient-Based Adversarial Attack
    Qiang Hu, Yuejun Guo, Maxime Cordy, Mike Papadakis and Yves Le Traon
    In Proc. 38th IEEE/ACM Conference on Automated Software Engineering (ASE 2023), NIER track
    
12. CodeS: A Distribution Shift Benchmark Dataset for Source Code Learning
    Qiang Hu, Yuejun Guo, Xiaofei Xie, Maxime Cordy, Lei Ma, Mike Papadakis, and Yves Le Traon
    In Proc. 45nd International Conference on Software Engineering (ICSE 2023), NIER track
    
13. DeepMutation++: A Mutation Testing Framework for Deep Learning Systems
    Qiang Hu, Lei Ma, Xiaofei Xie, Bing Yu, Yang Liu, and Jianjun Zhao
    In Proc. 34th IEEE/ACM Conference on Automated Software Engineering (ASE 2019), Tool Demo Track
    
14. DeepGraph: A Pycharm Tool for Visualizing and Understanding Deep Learning Models
    Qiang Hu, Lei Ma, and Jianjun Zhao
    In 25th Asia-Pacific Software Engineering Conference (APSEC 2018), ERA Track
    
15. Unveiling Code Pre-Trained Models: Investigating Syntax and Semantics Capacities.
    Wei Ma, Shangqing Liu, Mengjie Zhao, Xiaofei Xie, Wenhan Wang, Qiang Hu, Jie Zhang, and Yang Liu.
    In ACM Transactions on Software Engineering and Methodology (TOSEM) 2024.
    
16. RNNS: Representation Nearest Neighbor Search Black-Box Attack on Code Models
    Jie Zhang, Wei Ma, Qiang Hu, Xiaofei Xie, Yves Le Traon, and Yang Liu.
    In the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023-Findings).
    
17. KAPE: kNN-Based Performance Testing for Deep Code Search
    Yuejun Guo, Qiang Hu*, Xiaofei Xie, Maxime Cordy, Mike Papadakis, and Yves Le Traon.
    In ACM Transactions on Software Engineering and Methodology (TOSEM) 2023.
    
18. On the Effectiveness of Graph Data Augmentation for Source Code Learning
    Zeming Dong, Qiang Hu, Zhenya Zhang, and Jianjun Zhao.
    In Knowledge-Based Systems (KBS 2023).
    
19. An Empirical Study of the Imbalance Issue in Software Vulnerability Detection
    Yuejun Guo, Qiang Hu*, Qiang Tang, and Yves Le Traon.
    In the 28th European Symposium on Research in Computer Security (ESORICS) 2023.
    
20. MixCode: Enhancing Code Classification by Mixup-Based Data Augmentation
    Zeming Dong, Qiang Hu*, Yuejun Guo, Maxime Cordy, Mike Papadakis, Zhenya Zhang, Yves Le Traon, and Jianjun Zhao.
    In 30th International Conference on Software Analysis, Evolution and Reengineering (SANER 2023).
    
21. DRE: Density-Based Data Selection With Entropy for Adversarial-Robust Deep Learning Models
    Yuejun Guo, Qiang Hu, Maxime Cordy, Mike Papadakis, and Yves Le Traon.
    In Neural Computing and Applications (NCAA).
    
22. GraphCode2Vec: Generic Code Embedding via Lexical and Program Dependence Analyses
    Wei Ma, Mengjie Zhao, Ezekiel Soremekun, Qiang Hu, Jie Zhang, Mike Papadakis, Maxime Cordy, Xiaofei Xie, Yves Le Traon.
    In Proc. 19th International Conference on Mining Software Repositories (MSR 2022).
    
23. Robust Active Learning: Sample-Efficient Training of Robust Deep Learning Models
    Yuejun Guo, Qiang Hu, Mike Papadakis and Yves Le Traon.
    In Proc. 1st International Conference on AI Engineering - Software Engineering for AI (CAIN 2022).
    
24. Towards Characterizing Adversarial Defects of Deep Learning Software from the Lens of Uncertainty
    Xiyue Zhang, Xiaofei Xie, Lei Ma, Xiaoning Du, Qiang Hu, Yang Liu, Jianjun Zhao and Meng Sun.
    In Proc. 42nd International Conference on Software Engineering (ICSE 2020).
    
25. An Empirical Study towards Characterizing Deep Learning Development and Deployment across Different Frameworks and Platforms
    Qianyu Guo, Sen Chen, Xiaofei Xie, Lei Ma, Qiang Hu, Hongtao Liu, Yang Liu, Jianjun Zhao, Xiaohong Li.
    In Proc. 34th IEEE/ACM Conference on Automated Software Engineering (ASE 2019).
    

Preprint

1. MuCS: Enhancing Fault Detection for Large Language Models via Mutation-Based Confidence Smoothing
   Qiang Hu, Jin Wen, Maxime Cordy, Yuheng Huang, Xiaofei Xie, and Lei Ma
   Preprint. https://arxiv.org/pdf/2404.14419.pdf
   
2. Evaluating the Robustness of Test Selection Methods for Deep Neural Networks
   Qiang Hu, Yuejun Guo, Xiaofei Xie, Maxime Cordy, Wei Ma, Mike Papadakis, and Yves Le Traon.
   Preprint. https://arxiv.org/pdf/2308.01314.pdf.
   
3. A General Data Augmentation Framework for Boosting Deep Learning-Based Code Understanding
   Zeming Dong, Qiang Hu, Xiaofei Xie, Maxime Cordy, Mike Papadakis, and Jianjun Zhao.
   Preprint. https://arxiv.org/pdf/2402.15769.pdf.
   
4. Boosting Source Code Learning with Data Augmentation: An Empirical Study
   Zeming Dong, Qiang Hu, Yuejun Guo, Zhenya Zhang, Maxime Cordy, Mike Papadakis, Yves Le Traon, and Jianjun Zhao.
   Preprint. https://arxiv.org/pdf/2303.06808.pdf.
   
5. Capabilities of ChatGPT for Code Analysis: An Empirical Study
   Wei Ma, Shangqing Liu, Wenhan Wang, Qiang Hu, Ye Liu, Cen Zhang, Liming Nie, and Yang Liu.
   Preprint. https://arxiv.org/pdf/2305.12138.pdf.
   

Education

Ph.D. in Computer Science, University of Luxembourg. 2020/09 - 2023/12. Supervisor: Prof. Yves Le Traon, Co-Supervisors: Dr. Maxime Cordy and Prof. Mike Papadakis

Master of Information Science, Kyushu University. 2018/04 - 2020/03. Supervisor: Prof. Jianjun Zhao

Research student, Kyushu University. 2017/10 - 2018/03. Supervisor: Prof. Jianjun Zhao

Bachelor of Information and Software Engineering, UESTC. 2013/09 - 2017/06

Honors & Awards

• NEC C&C Grants for Researchers Attending International Conferences (200,000 JPY), 2019
• People's Scholarship (UESTC), 2014, 2015

Invited Talk

• Efficient Testing and Maintenance of Deep Neural Networks
  Xi'an Jiaotong University
  Jnue 2023
  

Projects

STELLAR: Testing Self-Learning Systems, FNR, 2020-2023

Coverage Guided Anomaly Detection, Mitsubishi, 2019-2020

Services

PC member: ASE 2024, Internetware 2024, Forge 2024, ISSTA AE 2024, 2023
External-reviewer: ICSE 2023, 2022, 2021, 2020.
Reviewer: NeurIPS Datasets and Benchmarks track 2024, 2023, 2022. Transactions on Big Data. Neural Computing and Applications. Automated Software Engineering

Teaching

Spring 2019, Teaching Assistant in The Concepts of Programming Languages, instructed by Prof. Jianjun Zhao

© 2023 Qiang Hu. Template from https://p2333.github.io/