Qiang Hu   Associate Professor (副教授，博导) College of Intelligence and Computing, School of Cyber Security Tianjin University Email: qianghu0515[at]gmail[dot]com

Biography

I am an Associate Professor at Tianjin University. Previously, I was a Postdoctoral researcher 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, Prof. Mike Papadakis, and Prof. Maxime Cordy. Before that, I received my Master degree from Kyushu University, advised by Prof. Jianjun Zhao.

My research interests span the areas of software engineering and deep learning, including SE4AI and AI4SE.
Our recent survey about label-efficient testing of DL can be found at survey .

欢迎对可信人工智能、软件安全以及智能软件工程相关研究感兴趣的同学与我联系,可以发送个人简历到我的邮箱或与我约时间交流.长期招收本科生进入实验室参与科研实习。

Preprint

• Data-Efficient Large Language Model Training: A Survey
  Xinyang Liu, Qiang Hu, Yujie Ma, Zhenheng Tang, Tianlin Li, Yao Zhang, Junjie Wang, Hao Li, Yongqiang Lyu, and Yves Le Traon.
  
• Recent Advances in Deep Learning Kernel Optimization Using Large Language Models
  Tianlin Li, Chenxi Yang, Qitong Sun, Xiaoyu Zhang, Qiang Hu, Zhe Tang, Sheng Chen, Fei Yang, Aishan Liu, Xianglong Liu, Chao Shen, Yves Le Traon, and Yang Liu.
  

Selected Publications

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

• Foundation Models for Autonomous Driving System: An Initial Roadmap
  Xiongfei Wu, Mingfei Cheng, Xiaoning Ren, Qiang Hu*, Jianlang Chen, Yuheng Huang, Maxime Cordy, Yao Zhang, Xiaofei Xie, Lei Ma, Yves Le Traon.
  In ACM Transactions on Software Engineering and Methodology (TOSEM) 2026
• BACHunter: Detecting Broken Access Control Vulnerabilities in Intelligent Connected Vehicles
  Yanbang Sun, Xiaohong Li, Quanzhou Wang, Hebo Leng, Guangzheng Yao, Zhihua Xie, Qiang Hu, Junjie Wang.
  In 47th IEEE Symposium on Security and Privacy (S&P 2026).
  
• LIMR: Intent-Aware Mashup API Recommendation via LLM-Augmented Multi-Scale Fusion
  Yao Zhang, Yude Bai, Minhong Dong, Keqing Cen, Ji Zhang, Qiang Hu*, Wei Ma, Yongqiang Lyu, Ruitao Feng, Xiaohong Li, Junjie Wang, Lingxiao Jiang, Yang Liu.
  In Transactions on Services Computing (TSC) 2026.
  
• CodeS+: Towards Assessing the Generalization Ability of Code Models Under Distribution Shift
  Ziyue Shi, Junjie Wang, Yuejun Guo, Xiaofei Xie, Qiang Hu*, Maxime Cordy, Sen Chen, Mike Papadakis, Yves Le Traon, and Yongqiang Lyu.
  In IEEE Transactions on Software Engineering (TSE) 2026.
  
• Chimera: Harnessing Multi-Agent LLMs for Automatic Insider Threat Simulation
  Jiongchi Yu, Xiaofei Xie, Qiang Hu*, Yuhan Ma, and Ziming Zhao*.
  In the Network and Distributed System Security Symposium (NDSS 2026)
  
• On the Evaluation of Capability Estimation Methods for Large Language Models
  Qiang Hu, Jin Wen, Yao Zhang, Maxime Cordy, and Yongqiang Lyu.
  In the 40th Annual AAAI Conference on Artificial Intelligence (AAAI 2026) Oral
  
• Exploring the Power of Diffusion Large Language Models for Software Engineering: An Empirical Investigation
  Jingyao Zhang, Tianlin Li, Xiaoyu Zhang, Qiang Hu, and Bin Shi.
  In the 48th IEEE/ACM International Conference on Software Engineering (ICSE 2026), NIER track.
• GenCode: A Generic Data Augmentation Framework for Boosting Deep LearningBased Code Understanding
  Zeming Dong, Qiang Hu*, Xiaofei Xie, Maxime Cordy, Mike Papadakis, Yves Le Traon and Jianjun Zhao.
  In Empirical Software Engineering (EMSE) 2026.
• Exploring Neural Network Structure Code Reuse in the Open-Source Community for Improving Maintenance
  Xiaoning Ren, Yuekun Wang, Chongyang Liu, Yueming Wu, Qiang Hu, Lijun Zhang and Yinxing Xue.
  In Journal of Software: Evolution and Process 2026.
• Defects4C: Benchmarking Large Language Model Repair Capability with C/C++ Bugs
  Jian Wang, Xiaofei Xie, Qiang Hu*, Shangqing Liu*, Jiongchi Yu, Jiaolong Kong, and Yi Li.
  In Proc. 40th IEEE/ACM Conference on Automated Software Engineering (ASE 2025)
  
• Do Code Semantics Help? A Comprehensive Study on Execution Trace-Based Information for Code Large Language Models
  Jian Wang, Xiaofei Xie, Qiang Hu*, Shangqing Liu, and Yi Li.
  In the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP 2025-Findings).
  
• AcTracer: Active Testing of Large Language Model via Multi-Stage Sampling
  Yuheng Huang, Jiayang Song, Qiang Hu, Felix Xu, and Lei Ma.
  In ACM Transactions on Software Engineering and Methodology (TOSEM) 2025
  
• CAShift: Benchmarking Log-Based Cloud Attack Detection under Normality Shift
  Jiongchi Yu, Xiaofei Xie, Qiang Hu*, Bowen Zhang, Ziming Zhao, Yun Lin, Lei Ma, Ruitao Feng, and Frank Liau.
  In the ACM International Conference on the Foundations of Software Engineering (FSE) 2025
  
• DSBox: A Data Selection Framework for Efficient Deep Code Learning
  Xinyang Liu, Lili Quan, and Qiang Hu*.
  In Proc. 40th IEEE/ACM Conference on Automated Software Engineering (ASE 2025), Tool Demo Track
  
• An Analytical Perspective on Software Engineering for Large Language Models
  Tianlin Li, Qiang Hu*, Chong Wang, Jian Zhang, Wei Ma, Aishan Liu, Jingyi Wang, Yang Liu
  In 29th International Conference on Engineering of Complex Computer Systems (ICECCS 2025)
  
• Evaluation and Improvement of Test Selection for Large Language Models
  Lili Quan, Jin Wen, Qiang Hu*, Maxime Cordy, Yuheng Huang, Lei Ma, and Xiaohong Li.
  In Journal of Software: Evolution and Process 2025
  
• Quantum Software Engineering in Large Language Model Era: Testing, Generation, and Repair
  Jiaming Ye, Qiang Hu, Fuyuan Zhang, Xiongfei Wu, Jianjun Zhao, and Yuanshun Dai.
  2030 Software Engineering, co-located with FSE 2025
  
• Large Language Model Supply Chain: Open Problems From the Security Perspective
  Qiang Hu, Xiaofei Xie, Sen Chen, Lili Quan, and Lei Ma
  In the 1st International Workshop on Large Language Model Supply Chain Analysis (LLMSC), co-located with ISSTA 2025
  
• CKGFuzzer: LLM-Based Fuzz Driver Generation Enhanced By Code Knowledge Graph
  Hanxiang Xu, Wei Ma, Ting Zhou, Yanjie Zhao, Kai Chen, Qiang Hu, Yang Liu, and Haoyu Wang.
  In Proc. 47th International Conference on Software Engineering (ICSE 2025), Industry Challenge Track Best Paper Award
• Variable Renaming-Based Adversarial Test Generation for Code Model: Benchmark and Enhancement
  Jin Wen, Qiang Hu*, Yuejun Guo, Maxime Cordy, and Yves Le Traon.
  In ACM Transactions on Software Engineering and Methodology (TOSEM) 2025
  
• Boosting Source Code Learning with Text-Oriented Data Augmentation: An Empirical Study
  Zeming Dong, Qiang Hu*, Yuejun Guo, Zhenya Zhang, Maxime Cordy, Mike Papadakis, Yves Le Traon, and Jianjun Zhao.
  In Empirical Software Engineering (EMSE) 2025
  
• Assessing the Robustness of Test Selection Methods for Deep Neural Networks
  Qiang Hu, Yuejun Guo, Xiaofei Xie, Maxime Cordy, Wei Ma, Mike Papadakis, Lei Ma, and Yves Le Traon.
  In ACM Transactions on Software Engineering and Methodology (TOSEM) 2025
  
• 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
  
• 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.
  
• 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
  
• 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
  
• 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.
  
• 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. 45th 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
  
• 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
  
• 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.
  
• 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. 45th International Conference on Software Engineering (ICSE 2023), NIER track
  
• 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).
  
• 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
  
• 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).
  
• 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)
  
• 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).
  
• 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
  

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

• Best Paper Award at ML4CS 2025
• Best Industry Challenge Paper Award at ICSE 2025
• NEC C&C Grants for Researchers Attending International Conferences (200,000 JPY), 2019

Invited Talk

• 以数据为中心的人工智能系统测评与维护
  科大讯飞星火论坛
  2026年1月
  
• 以数据为中心的代码大模型测评
  大模型可靠性与安全性论坛·ChinaSoft 2025
  2025年11月
  
• Keynote: Data-Centric Evaluation of Large Code Models
  Machine Learning for Cybersecurity (MLC) Workshop, co-located with ICDM 2025
  Nov 2025
  
• 数据分布偏移下的人工智能系统测评与维护
  第40次全国计算机安全学术交流会·人工智能与云安全分论坛
  2025年9月
  
• Efficient Testing and Maintenance of Deep Neural Networks
  Xi'an Jiaotong University
  June 2023
  

Services

Organizers: E-SARS 2025 (co-located with NeurIPS 2025)
PC member: ICSE 2027, ISSRE 2026 RENE track, ASE 2026, ISSTA 2026, ICECCS 2025, ASE 2025, ASE 2024, Internetware 2024, Forge 2024, ISSTA AE track 2024, 2023
Reviewer: TSE, TOSEM, EMSE, ASE(J), JSME. NeurIPS Datasets and Benchmarks track 2025, 2024, 2023, 2022.

Teaching

Spring 2026, Trustworthy AI
Spring 2019, Teaching Assistant in The Concepts of Programming Languages, instructed by Prof. Jianjun Zhao

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