140 citations · 258 across the 12 of their papers we have counts for
6 papers · 1 filter
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…