Software Engineering Research · NUDT

RESET Group
Ecological · Emerging · Efficient · Evolutionary

Welcome to the RESET Group at the College of Computer, National University of Defense Technology (NUDT), led by Prof. Yang Zhang (张洋).

RESET = Research on Ecological / Emerging / Efficient / Evolutionary Software Engineering Technologies
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Quick Stats

CCF-A Publications6+
Accepted (2026)2
Active Members9
Research Areas6
Yang Zhang
Prof. Yang ZhangGroup Leader · NUDT
About RESET

Who we are & what we do

RESET Group is a research team led by Prof. Yang Zhang at the College of Computer, NUDT (Changsha, China). Our mission is to advance software engineering through software ecosystems, open-source technologies, DevOps automation, and AI-augmented engineering.

We combine empirical software engineering, mining software repositories, and data-driven tooling to tackle real-world challenges in modern software development. Our work has been published in top venues including ICSE, FSE, ASE, ISSTA, TSE, TOSEM.

Latest News

Recent milestones (see all)

Aug 2026
Two papers accepted to ASE 2026 (CCF-A). ASE 2026
Jul 2026
Scholar citations reached 1000+. Milestone
May 2026
Paper on deprecated NPM packages accepted to ISSTA 2026 (CCF-A). ISSTA 2026
Jan 2026
Selected as Young Editorial Board Member of 《计算机工程与科学》.
Oct 2025
Nominated for ACM China Rising Star Award (Top-5 nationwide). Award
Featured Publications

Recent highlights — full list on Research Page

2026
✓ Accepted

Looks Good, But Does It Run? A Large-Scale Empirical Study of the Executability of Example Code in Model Cards

Simeng Yao, Jialin Zhao, Yang Zhang†, Tun Li, Tao Wang, Changrong Xie, Zezhou Tang, Yiwen Wu

ASE 2026 CCF-AConference

2026
✓ Accepted

Deprecated but Not Abandoned: A Large-Scale Empirical Study on Growing-user-demand Deprecated NPM Packages

Zezhou Tang, Yang Zhang†, Xinjun Mao, Tanghaoran Zhang, Changrong Xie, Wenyu Xu, Simeng Yao, Yiwen Wu

ISSTA 2026 CCF-AConference

2025

DockerFill: Automatically Completing Dockerfile Code with Syntax-aware Multi-task Learning

Yiwen Wu, Yang Zhang†, Tao Wang, Bo Ding, Huaimin Wang

IEEE TSE CCF-AJournal