Pairwise GUI dataset construction between Android phones and tablets

Han Hu, Haolan Zhan, Yujin Huang, Di Liu

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

In the current landscape of pervasive smartphones and tablets, apps frequently exist across both platforms. Although apps share most graphic user interfaces (GUIs) and functionalities across phones and tablets, developers often rebuild from scratch for tablet versions, escalating costs and squandering existing design resources. Researchers are attempting to collect data and employ deep learning in automated GUIs development to enhance developers' productivity. 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 introduce the Papt dataset, a pioneering pairwise GUI dataset tailored for Android phones and tablets, encompassing 10, 035 phone-tablet GUI page pairs sourced from 5, 593 unique app pairs. We propose novel pairwise GUI collection approaches for constructing this dataset and delineate its advantages over currently prevailing datasets in the field. Through preliminary experiments on this dataset, we analyze the present challenges of utilizing deep learning in automated GUI development.

Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 36 (NeurIPS 2023)
EditorsA. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, S. Levine
Place of PublicationSan Diego CA USA
PublisherNeural Information Processing Systems (NIPS)
Number of pages13
Publication statusPublished - 2023
EventAdvances in Neural Information Processing Systems 2023 - Ernest N. Morial Convention Center, New Orleans, United States of America
Duration: 10 Dec 202316 Dec 2023
Conference number: 37th
https://openreview.net/group?id=NeurIPS.cc/2023/Conference#tab-accept-oral
https://neurips.cc/ (Website)
https://papers.nips.cc/paper_files/paper/2023 (Proceedings)

Publication series

NameAdvances in Neural Information Processing Systems
PublisherNeural Information Processing Systems (NIPS)
Volume36
ISSN (Print)1049-5258

Conference

ConferenceAdvances in Neural Information Processing Systems 2023
Abbreviated titleNeurIPS 2023
Country/TerritoryUnited States of America
CityNew Orleans
Period10/12/2316/12/23
Internet address

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