8 citations · 17 across the 11 of their papers we have counts for
12 papers
From Conversation to Contribution: Characterizing Coding Agent in Open-Source Software
Zihan Fang, Yueke Zhang, Ningzhi Tang +3
AI coding assistants such as GitHub Copilot and Cursor have evolved from code-suggestion tools into conversational collaborators, enabling vibe-coding workflows in which developers…
An Empirical Study of Agent Skills for Healthcare: Practice, Gaps, and Governance
Gelei Xu, Ningzhi Tang, Xueyang Li +4
Healthcare automation is shaped by local procedures and organizational constraints, so agent capabilities rarely transfer unchanged across settings. Agent skills, self-contained di…
How Coding Agents Fail Their Users: A Large-Scale Analysis of Developer-Agent Misalignment in 20,574 Real-World Sessions
Ningzhi Tang, Chaoran Chen, Gelei Xu +5
AI coding agents increasingly act directly within software environments, yet existing analyses of their failures rely on benchmark trajectories that miss how developers actually ex…
Programming by Chat: A Large-Scale Behavioral Analysis of 11,579 Real-World AI-Assisted IDE Sessions
Ningzhi Tang, Chaoran Chen, Zihan Fang +6
IDE-integrated AI coding assistants, which operate conversationally within developers' working codebases with access to project context and multi-file editing, are rapidly reshapin…
Lessons from Real-World Deployment of a Cognition-Preserving Writing Tool: Students Actively Engage with Critical Thinking and Planning Affordances
Yinuo Yang, Zheng Zhang, Ningzhi Tang +5
AI-supported writing tools show strong potential for scaffolding students' learning of argumentative writing. Prior work has demonstrated the benefits of AI-supported cognitive sca…
NaturalEdit: Code Modification through Direct Interaction with Adaptive Natural Language Representation
Ningzhi Tang, David Meininger, Gelei Xu +4
Code modification requires developers to comprehend code, plan changes, articulate intent, and validate outcomes, making it cognitively demanding. While natural language (NL) code…
Exploring Direct Instruction and Summary-Mediated Prompting in LLM-Assisted Code Modification
Ningzhi Tang, Emory Smith, Yu Huang +2
This paper presents a study of using large language models (LLMs) in modifying existing code. While LLMs for generating code have been widely studied, their role in code modificati…
Enabling On-Device Learning via Experience Replay with Efficient Dataset Condensation
Gelei Xu, Ningzhi Tang, Jun Xia +2
Upon deployment to edge devices, it is often desirable for a model to further learn from streaming data to improve accuracy. However, extracting representative features from such d…
A Study on Developer Behaviors for Validating and Repairing LLM-Generated Code Using Eye Tracking and IDE Actions
Ningzhi Tang, Meng Chen, Zheng Ning +4
The increasing use of large language model (LLM)-powered code generation tools, such as GitHub Copilot, is transforming software engineering practices. This paper investigates how…
Programmer Visual Attention During Context-Aware Code Summarization
Robert Wallace, Aakash Bansal, Zachary Karas +4
Abridged: Programmer attention represents the visual focus of programmers on parts of the source code in pursuit of programming tasks. We conducted an in-depth human study with 10…
Semi-decentralized Federated Ego Graph Learning for Recommendation
Liang Qu, Ningzhi Tang, Ruiqi Zheng +4
Collaborative filtering (CF) based recommender systems are typically trained based on personal interaction data (e.g., clicks and purchases) that could be naturally represented as…
Single-shot Embedding Dimension Search in Recommender System
Liang Qu, Yonghong Ye, Ningzhi Tang +3
As a crucial component of most modern deep recommender systems, feature embedding maps high-dimensional sparse user/item features into low-dimensional dense embeddings. However, th…