Zhiqiang Yuan is currently a Ph.D. candidate at Fudan University. As a member of CodeWisdom Group, he holds a valuable opportunity to work under the guidance of Prof. Xin Peng. His research primarily focuses on the intersection of Software Engineering (SE) and Artificial Intelligence (AI), with a specific emphasis on the interplay between AI for Software Engineering (AI4SE) and Software Engineering for AI (SE4AI). His primary interest is leveraging advanced Artificial Intelligence methodologies, such as large language models and knowledge graphs, to address software engineering challenges and tackle the software engineering problems prevalent in AI applications and scenarios.
๐ฅ News
- ๐๐ Project-Level C-to-Rust Translation via Pointer Knowledge Graphs is accepted to FSE 2026.
- ๐๐ TransAgent: Enhancing LLM-Based Code Translation via Fine-Grained Execution Alignment is accepted to FSE 2026.
๐ Publications
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[Preprint] Does Pass Rate Tell the Whole Story? Evaluating Design Constraint Compliance in LLM-based Issue Resolution.
Kai Yu, Zhenhao Zhou, Junhao Zeng, Ying Wang, Xueying Du, Zhiqiang Yuan, Junwei Liu, Ziyu Zhou, Yujia Wang, Chong Wang, Xin Peng -
[FSE'26] Project-Level C-to-Rust Translation via Pointer Knowledge Graphs.
Zhiqiang Yuan, Wenjun Mao, Zhuo Chen, Xiyue Shang, Chong Wang, Yiling Lou, Xin Peng -
[FSE'26] TransAgent: Enhancing LLM-Based Code Translation via Fine-Grained Execution Alignment.
Zhiqiang Yuan, Weitong Chen, Hanlin Wang, Xin Peng, Zhenpeng Chen, Yiling Lou -
[FSE'24] Evaluating and Improving ChatGPT for Unit Test Generation.
Zhiqiang Yuan, Mingwei Liu, Shiji Ding, Kaixin Wang, Yixuan Chen, Xin Peng, Yiling Lou
In: Proceedings of the ACM International Conference on the Foundations of Software Engineering, to appear, July 2024, Brazil, Brazil
[arXiv Preprint Version] No More Manual Tests? Evaluating and Improving ChatGPT for Unit Test Generation -
[Preprint] Evaluating Instruction-Tuned Large Language Models on Code Comprehension and Generation.
Zhiqiang Yuan, Junwei Liu, Qiancheng Zi, Mingwei Liu, Xin Peng, Yiling Lou -
[TOSEM'23] FQN Inference in Partial Code by Prompt-tuned Language Model of Code.
In: ACM Transactions on Software Engineering and MethodologyVolume 33Issue 2Article No.: 31pp 1โ32
Qing Huang, Zhiqiang Yuan, Zhenchang Xing, Xin Peng, Qinghua Lu -
[TKDE'24] SE Factual Knowledge in Frozen Giant Code Model: A Study on FQN and its Retrieval.
Qing Huang, Dianshu Liao, Zhenchang Xing, Zhiqiang Yuan, Qinghua Lu, Xiwei Xu, Jiaxing Lu. -
[ASE'22] Prompt-tuned Code Language Model as a Neural Knowledge Base for Type Inference in Statically-Typed Partial Code.
In: Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering
Qing Huang, Zhiqiang Yuan, Zhenchang Xing, Xiwei Xu, Liming Zhu, Qinghua Lu -
[TSC'22] 1+1>2: Programming Know-What and Know-How Knowledge Fusion, Semantic Enrichment and Coherent Application.
In: IEEE Transactions on Services Computing
Qing Huang, Zhiqiang Yuan, Zhenchang Xing, Zhengkang Zuo, Changjing Wang, Xin Xia