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].

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
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.
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
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
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)
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
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
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)
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
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
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
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
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.
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).
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.
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).
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.
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).
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).
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).
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).
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).
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).
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.
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.
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.
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.
Preprint. https://arxiv.org/pdf/2210.03123.pdf.
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

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 2024, 2023, 2022. Transactions on Big Data. Neural Computing and Applications.

Teaching

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