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With the popularity of smartphones and tablets, users have become accustomedto using different devices for different tasks, such as using their phones toplay games and tablets to watch movies. To conquer the market, one app is oftenavailable on both smartphones and tablets. However, although one app hassimilar graphic user interfaces (GUIs) and functionalities on phone and tablet,current app developers typically start from scratch when developing atablet-compatible version of their app, which drives up development costs andwastes existing design resources. Researchers are attempting to employ deeplearning in automated GUIs development to enhance developers' productivity.Deep learning models rely heavily on high-quality datasets. There are currentlyseveral publicly accessible GUI page datasets for phones, but none for pairwiseGUIs between phones and tablets. This poses a significant barrier to theemployment of deep learning in automated GUI development. In this paper, wecollect and make public the Papt dataset, which is a pairwise dataset for GUIconversion and retrieval between Android phones and tablets. The datasetcontains 10,035 phone-tablet GUI page pairs from 5,593 phone-tablet app pairs.We illustrate the approaches of collecting pairwise data and statisticalanalysis of this dataset. We also illustrate the advantages of our datasetcompared to other current datasets. Through preliminary experiments on thisdataset, we analyse the present challenges of utilising deep learning inautomated GUI development and find that our dataset can assist the applicationof some deep learning models to tasks involving automatic GUI development.

A Pairwise Dataset for GUI Conversion and Retrieval between Android Phones and Tablets