With the popularity of smartphones and tablets, users have become accustomed to using different devices for different tasks, such as using their phones to play games and tablets to watch movies. To conquer the market, one app is often available on both smartphones and tablets. However, although one app has similar graphic user interfaces (GUIs) and functionalities on phone and tablet, current app developers typically start from scratch when developing a tablet-compatible version of their app, which drives up development costs and wastes existing design resources. Researchers are attempting to employ deep learning in automated GUIs development to enhance developers' productivity. Deep learning models rely heavily on high-quality datasets. There are currently several publicly accessible GUI page datasets for phones, but none for pairwise GUIs between phones and tablets. This poses a significant barrier to the employment of deep learning in automated GUI development. In this paper, we collect and make public the Papt dataset, which is a pairwise dataset for GUI conversion and retrieval between Android phones and tablets. The dataset contains 10,035 phone-tablet GUI page pairs from 5,593 phone-tablet app pairs. We illustrate the approaches of collecting pairwise data and statistical analysis of this dataset. We also illustrate the advantages of our dataset compared to other current datasets. Through preliminary experiments on this dataset, we analyse the present challenges of utilising deep learning in automated GUI development and find that our dataset can assist the application of some deep learning models to tasks involving automatic GUI development.

翻译：随着智能手机和平板电脑的普及，用户已习惯根据任务需求使用不同设备，例如用手机玩游戏、用平板看电影。为占领市场，同一应用常需同时适配手机和平板。然而，尽管同一应用在手机和平板上的图形用户界面（GUI）及功能高度相似，当前开发者从零开始开发平板兼容版本的现象普遍存在，这导致开发成本攀升且浪费现有设计资源。研究者正尝试利用深度学习实现GUI自动化开发以提升效率，而深度学习模型高度依赖高质量数据集。尽管目前存在多个公开的手机GUI页面数据集，但尚无针对手机和平板配对的GUI数据集，这成为深度学习应用于GUI自动化开发的主要障碍。本文收集并公开了Papt数据集——一个用于安卓手机与平板间GUI转换与检索的配对数据集。该数据集包含来自5,593对手机-平板应用的10,035组GUI页面配对。我们阐述了配对数据的收集方法与统计分析过程，并展示了该数据集相较现有同类数据的优势。通过在该数据集上的初步实验，我们分析了当前将深度学习应用于GUI自动化开发面临的挑战，并验证了本数据集可支持部分深度学习模型在自动GUI开发任务中的应用。