20 citations · 40 across the 5 of their papers we have counts for
5 papers
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…
HDLdebugger: Streamlining HDL debugging with Large Language Models
Xufeng Yao, Haoyang Li, Tsz Ho Chan +5
In the domain of chip design, Hardware Description Languages (HDLs) play a pivotal role. However, due to the complex syntax of HDLs and the limited availability of online resources…
EyeTrans: Merging Human and Machine Attention for Neural Code Summarization
Yifan Zhang, Jiliang Li, Zachary Karas +5
Neural code summarization leverages deep learning models to automatically generate brief natural language summaries of code snippets. The development of Transformer models has led…
Do Machines and Humans Focus on Similar Code? Exploring Explainability of Large Language Models in Code Summarization
Jiliang Li, Yifan Zhang, Zachary Karas +3
Recent language models have demonstrated proficiency in summarizing source code. However, as in many other domains of machine learning, language models of code lack sufficient expl…
Modeling Programmer Attention as Scanpath Prediction
Aakash Bansal, Chia-Yi Su, Zachary Karas +4
This paper launches a new effort at modeling programmer attention by predicting eye movement scanpaths. Programmer attention refers to what information people intake when performin…