20 citations · 48 across the 17 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…