一句话：提出用眼动扫描路径预测来建模程序员注意力，并开展眼动实验与原型预测器验证。
本文旨在通过预测眼动扫描路径来建模程序员注意力。程序员注意力指人在编程任务中吸收哪些信息；其模型可用于构建更好的界面、辅助技术和更像人的 AI。过去软件工程研究多依据鼠标点击、键盘日志和 IDE 交互等特征建模，但该领域的终极目标是扫描路径预测，即预测人在视觉刺激上的注视序列，因为眼动被认为是信息摄入的最具体证据。扫描路径预测非常困难，作者认为低成本高精度眼动设备与源代码大语言模型的出现使解决方案成为可能。为此，他们开展了一项 27 名程序员参与的眼动实验，并给出一个原型扫描路径预测器，以展示初步结果并获取社区早期反馈。
This paper launches a new effort at modeling programmer attention by predicting eye movement scanpaths. Programmer attention refers to what information people intake when performing programming tasks. Models of programmer attention refer to machine prediction of what information is important to people. Models of programmer attention are important because they help researchers build better interfaces, assistive technologies, and more human-like AI. For many years, researchers in SE have built these models based on features such as mouse clicks, key logging, and IDE interactions. Yet the holy grail in this area is scanpath prediction - the prediction of the sequence of eye fixations a person would take over a visual stimulus. A person's eye movements are considered the most concrete evidence that a person is taking in a piece of information. Scanpath prediction is a notoriously difficult problem, but we believe that the emergence of lower-cost, higheraccuracy eye tracking equipment and better large language models of source code brings a solution within grasp. We present an eye tracking experiment with 27 programmers and a prototype scanpath predictor to present preliminary results and obtain early community feedback.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{BansalSKZHLM23,
title = {Modeling Programmer Attention as Scanpath Prediction},
author = {Aakash Bansal and Chia-Yi Su and Zachary Karas and Yifan Zhang and Yu Huang and Toby Jia-Jun Li and Collin McMillan},
booktitle = {ASE},
year = {2023},
doi = {10.1109/ase56229.2023.00092}
}