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Seenomaly: vision-based linting of GUI animation effects against design-don't guidelines

Published: 01 October 2020 Publication History

Abstract

GUI animations, such as card movement, menu slide in/out, snackbar display, provide appealing user experience and enhance the usability of mobile applications. These GUI animations should not violate the platform's UI design guidelines (referred to as design-don't guideline in this work) regarding component motion and interaction, content appearing and disappearing, and elevation and shadow changes. However, none of existing static code analysis, functional GUI testing and GUI image comparison techniques can "see" the GUI animations on the scree, and thus they cannot support the linting of GUI animations against design-don't guidelines. In this work, we formulate this GUI animation linting problem as a multi-class screencast classification task, but we do not have sufficient labeled GUI animations to train the classifier. Instead, we propose an unsupervised, computer-vision based adversarial autoencoder to solve this linting problem. Our autoencoder learns to group similar GUI animations by "seeing" lots of unlabeled real-application GUI animations and learning to generate them. As the first work of its kind, we build the datasets of synthetic and real-world GUI animations. Through experiments on these datasets, we systematically investigate the learning capability of our model and its effectiveness and practicality for linting GUI animations, and identify the challenges in this linting problem for future work.

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cover image ACM Conferences
ICSE '20: Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering
June 2020
1640 pages
ISBN:9781450371216
DOI:10.1145/3377811
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • KIISE: Korean Institute of Information Scientists and Engineers
  • IEEE CS

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Published: 01 October 2020

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Author Tags

  1. GUI animation
  2. design guidelines
  3. lint
  4. unsupervised learning

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Overall Acceptance Rate 276 of 1,856 submissions, 15%

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Cited By

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  • (2024)UIGuider: Detecting Implicit Design Guidelines Using a Domain Knowledge Graph ApproachElectronics10.3390/electronics1307121013:7(1210)Online publication date: 26-Mar-2024
  • (2024)Less Cybersickness, Please: Demystifying and Detecting Stereoscopic Visual Inconsistencies in Virtual Reality AppsProceedings of the ACM on Software Engineering10.1145/36608031:FSE(2167-2189)Online publication date: 12-Jul-2024
  • (2024)Automating GUI-based Test Oracles for Mobile AppsProceedings of the 21st International Conference on Mining Software Repositories10.1145/3643991.3644930(309-321)Online publication date: 15-Apr-2024
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  • (2024)API Governance at ScaleProceedings of the 46th International Conference on Software Engineering: Software Engineering in Practice10.1145/3639477.3639713(430-440)Online publication date: 14-Apr-2024
  • (2024)AI Is Not Enough: A Hybrid Technical Approach to AI Adoption in UI Linting With HeuristicsExtended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613905.3637135(1-7)Online publication date: 11-May-2024
  • (2024)UiAnalyzerExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122408239:COnline publication date: 17-Apr-2024
  • (2024)GUI2DSVec: Detecting Visual Design Smells Based on Semantic Embedding of GUI Images and ComponentsMobile and Ubiquitous Systems: Computing, Networking and Services10.1007/978-3-031-63992-0_29(445-457)Online publication date: 19-Jul-2024
  • (2023)What You See Is What You Get? It Is Not the Case! Detecting Misleading Icons for Mobile ApplicationsProceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3597926.3598076(538-550)Online publication date: 12-Jul-2023
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