Introduction

👀Name: Zhaoqiang Guo(郭肇强)

🎓Status: ZJU100 Young Professor

🏛️Institution: College of Computer Science and Technology, State Key Laboratory of Blockchain and Data Security, Zhejiang University; Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security

✉️E-mail: guozhaoqiang@zju.edu.cn / guozhaoqiang@bcds.org.cn / gzq.qiang@gmail.com

📍Address: Yuquan Campus, Zhejiang University, Hangzhou, China

🔗Links: GitHub Google Scholar ORCID DBLP

My name is Zhaoqiang Guo. I received my Bachelor’s degree from Nanjing University of Science and Technology in 2017, and obtained my Ph.D. from Nanjing University in 2022, under the supervision of Professor Yuming Zhou (周毓明). My doctoral research focused on Software Quality Assurance techniques. After graduation, I worked as a Senior Engineer at Huawei, focusing on AI for Software Engineering. Currently, I am a ZJU100 Young Professor at the State Key Laboratory of Blockchain and Data Security, Zhejiang University, where I conduct research on the intersection of blockchain and AI. Feel free to reach out for academic discussions.

Research

My current research focuses on the intersection of blockchain and artificial intelligence, building on a strong foundation in AI for software engineering and software quality assurance. I am interested in building intelligent, reliable, and trustworthy systems, and my work unfolds along three complementary directions:

Blockchain × AI

Investigating the intersection of blockchain and artificial intelligence — applying AI/LLM techniques to smart contract security and on-chain data analysis, and leveraging blockchain to build transparent, verifiable, and trustworthy AI systems.

AI for Software Engineering

Leveraging LLM and Agent techniques to automate and empower software engineering tasks, including code generation, unit test generation, bug localization, and code review.

Software Quality Assurance

Advancing software quality assurance techniques — defect prediction, static analysis warning mitigation, and technical debt management — through rigorous empirical studies and practical tooling.

Experience

News

• 2026-02-26. Journal paper accepted to TOSEM: A roadmap for modern code review: Challenges and opportunities.

Service

Peer Reviewer

• 📔Journals: TSE, EMSE, ASEJ, IJSEKE.

Organizing Committee

• 🧑‍💼Program Committee Member, Early Research Achievements (ERA) Track, 32nd Asia-Pacific Software Engineering Conference (APSEC 2026).

Publication

Remarks: 📕Journal paper, 📘Conference paper. 📗arXiv paper.

📚 International Papers

• 📗arXiv Qinghua Xu, Guancheng Wang, Lionel C. Briand*, Zhaoqiang Guo, Kui Liu. LLM-based Mockless Unit Test Generation for Java. 2026.

• 📗arXiv Guancheng Wang, Qinghua Xu, Lionel C. Briand*, Zhaoqiang Guo, Kui Liu. Call-Chain-Aware LLM-Based Test Generation for Java Projects. 2026.

• 📗arXiv Bei Chu, Yang Feng*, Kui Liu, Zhaoqiang Guo, Yichi Zhang, Hange Shi, Zifan Nan, Baowen Xu. Large Language Models for Unit Test Generation: Achievements, Challenges, and Opportunities. 2025.

• 📕CCF-A Zezhou Yang, Cuiyun Gao*, Zhaoqiang Guo*, Zhenhao Li, Kui Liu, Xin Xia, Yuming Zhou. A roadmap for modern code review: Challenges and opportunities. ACM Transactions on Software Engineering and Methodology, 2026-02-26, Accepted.

• 📘CCF-A Bei Chu, Yang Feng*, Kui Liu, Hange Shi, Zifan Nan, Zhaoqiang Guo, Baowen Xu. PALM: Synergizing Program Analysis and LLMs to Enhance Rust Unit Test Coverage. ASE-2025 2720-2732.

• 📘CCF-A Zifan Nan, Zhaoqiang Guo, Kui Liu, Xin Xia*. Test Intention Guided LLM-based Unit Test Generation. ICSE-2025 1026-1038.

• 📘CCF-A Di Wu, Lin Shi*, Zhaoqiang Guo, Kui Liu, Weiguang Zhuang,Yuqi Zhong, Li Zhang. iSMELL: Assembling LLMs with Expert Toolsets for Code Smell Detection and Refactoring. ASE-2024. 1345-1357.

• 📕CCF-B Yufei Zhou, Xutong Liu, Zhaoqiang Guo, Yuming Zhou*, Corey Zhang, Junyan Qian. Deep learning or classical machine learning? An empirical study on line-level software defect prediction. Journal of Software: Evolution and Process, 2024, 36(10).

• 📕CCF-B Xutong Liu, Shiran Liu, Zhaoqiang Guo, Peng Zhang, Yibiao Yang, Huihui Liu, Hongmin Lu, Yanhui Li, Lin Chen, Yuming Zhou*. Towards a framework for reliable performance evaluation in defect prediction. Science of Computer Programming, 2024, 238:103164.

• 📕CCF-C Yuanqing Mei, Yi Rong, Shiran Liu, Zhaoqiang Guo, Yibiao Yang, Hongmin Lu, Yutian Tang, Yuming Zhou*.Deriving thresholds of object-oriented metrics to predict defect-proneness of classes: A large-scale meta-analysis. International Journal of Software Engineering and Knowledge Engineering, 2023, 33(5), 651-695.

• 📕CCF-A Shiran Liu, Zhaoqiang Guo, Yanhui Li, Chuanqi Wang, Lin Chen, Zhongbin Sun, Yuming Zhou*, Baowen Xu. Inconsistent defect labels: Essential, causes, and influence. IEEE Transactions on Software Engineering. 2023, 49(2), 586-610.

• 📕CCF-A Zhaoqiang Guo, Tingting Tan, Shiran Liu, Xutong Liu, Wei Lai, Yanhui Li, Lin Chen, Wei Dong, Yuming Zhou*. Mitigating false positive static analysis warnings: Progress, Challenges, and Opportunities. IEEE Transactions on Software Engineering. 2023, 49(12), 5154-5188.

• 📕CCF-A Zhaoqiang Guo, Shiran Liu, Xutong Liu, Wei Lai, Mingliang Ma, Xu Zhang, Chao Ni, Yibiao Yang, Yanhui Li, Lin Chen, Guoqiang Zhou, Yuming Zhou*. Code-line-level bugginess identification: How far have we come, and how far have we yet to go? ACM Transactions on Software Engineering and Methodology, 2022, 32(4), 102:1-102:55.

• 📕CCF-A Zhaoqiang Guo, Shiran Liu, Jinping Liu, Yanhui Li, Lin Chen, Hongmin Lu, Yuming Zhou*. How far have we progressed in identifying self-admitted technical debts? A comprehensive empirical study. ACM Transactions on Software Engineering and Methodology, 2021, 30(4), 45:1-45:56.

• 📕CCF-B Shiran Liu, Zhaoqiang Guo, Yanhui Li, Hongmin Lu, Lin Chen, Lei Xu, Yuming Zhou*, Baowen Xu. Prioritizing documentation effort: Can we do it simpler but better? Information of Software and Technology. 2021, 140.

• 📕CCF-B Zhaoqiang Guo, Yanhui Li, Wanwangying Ma, Yuming Zhou*, Hongmin Lu, Lin Chen, Baowen Xu. Boosting crash-inducing change localization with rank-performance-based feature subset selection. Empirical Software Engineering, 2020, 25(3), 1905-1950.

📚 Domestic Papers

• 📕CCF 中文A类 唐家昕, 王璇, 赖伟, 路则雨, 郭肇强, 杨已彪, 周毓明*. 深度学习驱动的软件漏洞预测: 问题、进展与挑战. 软件学报, 2025, 36(11), 4906–4952.

• 📕CCF 中文A类 路则雨, 张鹏, 王洋, 郭肇强, 杨已彪, 周毓明*. 测试集有效性评价：问题、进展与挑战. 软件学报, 2024, 35(2), 532–580.

• 📕CCF 中文A类 周慧聪, 郭肇强(共一), 梅元清, 李言辉*, 陈林, 周毓明*. 版本失配和数据泄露对基于缺陷报告的缺陷定位模型的影响. 软件学报, 2023, 34(5), 2196–2217.

• 📕CCF 中文A类 刘旭同, 郭肇强, 刘释然, 张鹏, 卢红敏, 周毓明*. 软件缺陷预测模型间的比较实验：问题、进展与挑战. 软件学报, 2023, 34(2), 582–624.

• 📕CCF 中文A类 梅元清, 郭肇强, 周慧聪, 李言辉, 陈林, 卢红敏, 周毓明*. 面向对象软件度量阈值的确定方法：问题、进展与挑战. 软件学报, 2023, 34(1), 50-102.

• 📕CCF 中文A类 郭肇强, 刘释然, 谭婷婷, 李言辉*, 陈林, 周毓明*, 徐宝文. 自承认技术债的研究: 问题、进展与挑战. 软件学报, 2022, 33(1), 26-54.

• 📕CCF 中文A类 郭肇强, 周慧聪, 刘释然, 李言辉*, 陈林, 周毓明*, 徐宝文. 基于信息检索的缺陷定位: 问题、进展与挑战. 软件学报, 2020, 31(9), 2826−2854.

Appendix

1. CCF推荐国际学术刊物目录

2. CCF推荐中文学术刊物目录

Last updated: July, 2026