140 citations · 258 across the 12 of their papers we have counts for
13 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…
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…
Sketchar: Supporting Character Design and Illustration Prototyping Using Generative AI
Long Ling, Xinyi Chen, Ruoyu Wen +2
Character design in games involves interdisciplinary collaborations, typically between designers who create the narrative content, and illustrators who realize the design vision. H…
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…
EyeMulator: Improving Code Language Models by Mimicking Human Visual Attention
Yifan Zhang, Chen Huang, Yueke Zhang +5
Code Language Models (CodeLLMs) learn token importance from data correlations, whereas human developers attend selectively to semantically salient code. We present EyeMulator, a mo…
AI-Mediated Code Comment Improvement
Maria Dhakal, Chia-Yi Su, Robert Wallace +5
This paper describes an approach to improve code comments along different quality axes by rewriting those comments with customized Artificial Intelligence (AI)-based tools. We cond…
Unequal Opportunities: Examining the Bias in Geographical Recommendations by Large Language Models
Shiran Dudy, Thulasi Tholeti, Resmi Ramachandranpillai +3
Recent advancements in Large Language Models (LLMs) have made them a popular information-seeking tool among end users. However, the statistical training methods for LLMs have raise…
Supporting Co-Adaptive Machine Teaching through Human Concept Learning and Cognitive Theories
Simret Araya Gebreegziabher, Yukun Yang, Elena L. Glassman +1
An important challenge in interactive machine learning, particularly in subjective or ambiguous domains, is fostering bi-directional alignment between humans and models. Users teac…
Context-aware Code Summary Generation
Chia-Yi Su, Aakash Bansal, Yu Huang +2
Code summary generation is the task of writing natural language descriptions of a section of source code. Recent advances in Large Language Models (LLMs) and other AI-based technol…
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…
A Taxonomy for Human-LLM Interaction Modes: An Initial Exploration
Jie Gao, Simret Araya Gebreegziabher, Kenny Tsu Wei Choo +3
With ChatGPT's release, conversational prompting has become the most popular form of human-LLM interaction. However, its effectiveness is limited for more complex tasks involving r…
Luminate: Structured Generation and Exploration of Design Space with Large Language Models for Human-AI Co-Creation
Sangho Suh, Meng Chen, Bryan Min +2
Thanks to their generative capabilities, large language models (LLMs) have become an invaluable tool for creative processes. These models have the capacity to produce hundreds and…
CollabCoder: A Lower-barrier, Rigorous Workflow for Inductive Collaborative Qualitative Analysis with Large Language Models
Jie Gao, Yuchen Guo, Gionnieve Lim +4
Collaborative Qualitative Analysis (CQA) can enhance qualitative analysis rigor and depth by incorporating varied viewpoints. Nevertheless, ensuring a rigorous CQA procedure itself…