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The Future of Computational Approaches for Understanding and Adapting User Interfaces

Published:19 April 2023Publication History

ABSTRACT

Building on the success of the first workshop on understanding, generating, and adapting user interfaces at CHI2022, this workshop will advance this research area further by looking at existing results and exploring new research directions. Computational approaches for user interfaces have been used in adapting interfaces for different devices, modalities, and user preferences. Recent work has made significant progress in understanding and adapting user interfaces with traditional constraint/rule-based optimization and machine learning-based data-driven approaches; however, these two approaches remain separate. Combining the two approaches has great potential to advance the area but remains under-explored and challenging. Other contributions, such as datasets for potential applications, novel representations of user interfaces, the analysis of human traces, and models with multi-modalities, will also open up future research options. The proposed workshop seeks to bring together researchers interested in computational approaches for user interfaces to discuss the needs and opportunities for future user interface algorithms, models, and applications.

References

  1. Greg J. Badros, Alan Borning, and Peter J. Stuckey. 2001. The Cassowary Linear Arithmetic Constraint Solving Algorithm. ACM Trans. Comput.-Hum. Interact 8, 4 (2001), 267–306. https://doi.org/10.1145/504704.504705Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Chongyang Bai, Xiaoxue Zang, Ying Xu, Srinivas Sunkara, Abhinav Rastogi, Jindong Chen, 2021. Uibert: Learning generic multimodal representations for ui understanding. arXiv preprint arXiv:2107.13731(2021).Google ScholarGoogle Scholar
  3. Pavol Bielik, Marc Fischer, and Martin Vechev. 2018. Robust Relational Layout Synthesis from Examples for Android. Proc. ACM Program. Lang. 2, OOPSLA, Article 156 (Oct. 2018), 29 pages. https://doi.org/10.1145/3276526Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Alan Borning and Robert Duisberg. 1986. Constraint-Based Tools for Building User Interfaces. ACM Trans. Graph. 5, 4 (Oct. 1986), 345–374. https://doi.org/10.1145/27623.29354Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Biplab Deka, Zifeng Huang, Chad Franzen, Joshua Hibschman, Daniel Afergan, Yang Li, Jeffrey Nichols, and Ranjitha Kumar. 2017. Rico: A Mobile App Dataset for Building Data-Driven Design Applications. In Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology (Québec City, QC, Canada) (UIST ’17). Association for Computing Machinery, New York, NY, USA, 845–854. https://doi.org/10.1145/3126594.3126651Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Morgan Dixon and James Fogarty. 2010. Prefab: Implementing Advanced Behaviors Using Pixel-Based Reverse Engineering of Interface Structure. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Atlanta, Georgia, USA) (CHI ’10). Association for Computing Machinery, New York, NY, USA, 1525–1534. https://doi.org/10.1145/1753326.1753554Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Krzysztof Gajos and Daniel Weld. 2005. Preference Elicitation for Interface Optimization. UIST: Proceedings of the Annual ACM Symposium on User Interface Softaware and Technology, 173–182. https://doi.org/10.1145/1095034.1095063Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Krzysztof Gajos and Daniel S. Weld. 2004. SUPPLE: Automatically Generating User Interfaces. In Proceedings of the 9th International Conference on Intelligent User Interfaces (Funchal, Madeira, Portugal) (IUI ’04). Association for Computing Machinery, New York, NY, USA, 93–100. https://doi.org/10.1145/964442.964461Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Krzysztof Z. Gajos, Daniel S. Weld, and Jacob O. Wobbrock. 2010. Automatically Generating Personalized User Interfaces With Supple, In Proceedings of the 9th International Conference on Intelligent User Interfaces. Artif. Intell 174, 12-13, 910–950. https://doi.org/10.1016/j.artint.2010.05.005Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Forrest Huang, John F. Canny, and Jeffrey Nichols. 2019. Swire: Sketch-Based User Interface Retrieval. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI ’19). Association for Computing Machinery, New York, NY, USA, 1–10. https://doi.org/10.1145/3290605.3300334Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Yue Jiang, Ruofei Du, Christof Lutteroth, and Wolfgang Stuerzlinger. 2019. ORC Layout: Adaptive GUI Layout with OR-Constraints. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI ’19). Association for Computing Machinery, New York, NY, USA, Article 413, 12 pages. https://doi.org/10.1145/3290605.3300643Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Yue Jiang, Yuwen Lu, Jeffrey Nichols, Wolfgang Stuerzlinger, Chun Yu, Christof Lutteroth, Yang Li, Ranjitha Kumar, and Toby Jia-Jun Li. 2022. Computational Approaches for Understanding, Generating, and Adapting User Interfaces. In CHI Conference on Human Factors in Computing Systems Extended Abstracts. 1–6.Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Yue Jiang, Wolfgang Stuerzlinger, and Christof Lutteroth. 2021. ReverseORC: Reverse Engineering of Resizable User Interface Layouts with OR-Constraints. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI ’21). Association for Computing Machinery, New York, NY, USA, Article 316, 18 pages. https://doi.org/10.1145/3411764.3445043Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Yue Jiang, Wolfgang Stuerzlinger, Matthias Zwicker, and Christof Lutteroth. 2020. ORCSolver: An Efficient Solver for Adaptive GUI Layout with OR-Constraints. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’20). Association for Computing Machinery, New York, NY, USA, 1–14. https://doi.org/10.1145/3313831.3376610Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Hsin-Ying Lee, Lu Jiang, Irfan Essa, Phuong B. Le, Haifeng Gong, Ming-Hsuan Yang, and Weilong Yang. 2020. Neural Design Network: Graphic Layout Generation with Constraints. In Computer Vision – ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part III (Glasgow, United Kingdom). Springer-Verlag, Berlin, Heidelberg, 491–506. https://doi.org/10.1007/978-3-030-58580-8_29Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Luis A. Leiva, Asutosh Hota, and Antti Oulasvirta. 2020. Enrico: A High-quality Dataset for Topic Modeling of Mobile UI Designs. In Proc. MobileHCI Adjunct.Google ScholarGoogle Scholar
  17. Gang Li, Gilles Baechler, Manuel Tragut, and Yang Li. 2022. Learning to Denoise Raw Mobile UI Layouts for Improving Datasets at Scale. In CHI Conference on Human Factors in Computing Systems. 1–13.Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Jianan Li, Jimei Yang, Aaron Hertzmann, Jianming Zhang, and Tingfa Xu. 2019. Layoutgan: Generating graphic layouts with wireframe discriminators. arXiv preprint arXiv:1901.06767(2019).Google ScholarGoogle Scholar
  19. Jianan Li, Jimei Yang, Jianming Zhang, Chang Liu, Christina Wang, and Tingfa Xu. 2021. Attribute-Conditioned Layout GAN for Automatic Graphic Design. IEEE Transactions on Visualization and Computer Graphics 27, 10 (oct 2021), 4039–4048. https://doi.org/10.1109/TVCG.2020.2999335Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Toby Jia-Jun Li, Amos Azaria, and Brad A. Myers. 2017. SUGILITE: Creating Multimodal Smartphone Automation by Demonstration. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 6038–6049. https://doi.org/10.1145/3025453.3025483Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Toby Jia-Jun Li, Igor Labutov, Xiaohan Nancy Li, Xiaoyi Zhang, Wenze Shi, Wanling Ding, Tom M. Mitchell, and Brad A. Myers. 2018. APPINITE: A Multi-Modal Interface for Specifying Data Descriptions in Programming by Demonstration Using Natural Language Instructions. In 2018 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC). 105–114. https://doi.org/10.1109/VLHCC.2018.8506506Google ScholarGoogle ScholarCross RefCross Ref
  22. Toby Jia-Jun Li, Lindsay Popowski, Tom Mitchell, and Brad A Myers. 2021. Screen2Vec: Semantic Embedding of GUI Screens and GUI Components. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI ’21). Association for Computing Machinery, New York, NY, USA, Article 578, 15 pages. https://doi.org/10.1145/3411764.3445049Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Toby Jia-Jun Li, Marissa Radensky, Justin Jia, Kirielle Singarajah, Tom M. Mitchell, and Brad A. Myers. 2019. PUMICE: A Multi-Modal Agent That Learns Concepts and Conditionals from Natural Language and Demonstrations. In Proceedings of the 32nd Annual ACM Symposium on User Interface Software and Technology (New Orleans, LA, USA) (UIST ’19). Association for Computing Machinery, New York, NY, USA, 577–589. https://doi.org/10.1145/3332165.3347899Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Toby Jia-Jun Li and Oriana Riva. 2018. KITE: Building conversational bots from mobile apps. In Proceedings of the 16th ACM International Conference on Mobile Systems, Applications, and Services (MobiSys 2018). ACM.Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Yang Li, Jiacong He, Xin Zhou, Yuan Zhang, and Jason Baldridge. 2020. Mapping Natural Language Instructions to Mobile UI Action Sequences. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. ACL, Online, 8198–8210. https://doi.org/10.18653/v1/2020.acl-main.729Google ScholarGoogle ScholarCross RefCross Ref
  26. Yang Li, Gang Li, Luheng He, Jingjie Zheng, Hong Li, and Zhiwei Guan. 2020. Widget Captioning: Generating Natural Language Description for Mobile User Interface Elements. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). ACL, Online, 5495–5510. https://doi.org/10.18653/v1/2020.emnlp-main.443Google ScholarGoogle ScholarCross RefCross Ref
  27. Thomas F. Liu, Mark Craft, Jason Situ, Ersin Yumer, Radomir Mech, and Ranjitha Kumar. 2018. Learning Design Semantics for Mobile Apps. In Proceedings of the 31st Annual ACM Symposium on User Interface Software and Technology (Berlin, Germany) (UIST ’18). Association for Computing Machinery, New York, NY, USA, 569–579. https://doi.org/10.1145/3242587.3242650Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Christof Lutteroth. 2008. Automated Reverse Engineering of Hard-Coded GUI Layouts. In Proceedings of the Ninth Conference on Australasian User Interface - Volume 76 (Wollongong, Australia) (AUIC ’08). Australian Computer Society, Inc., AUS, 65–73. https://doi.org/10.5555/1378337.1378350Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Kevin Moran, Ali Yachnes, George Purnell, Junayed Mahmud, Michele Tufano, Carlos Bernal Cardenas, Denys Poshyvanyk, and Zach H’Doubler. 2022. An Empirical Investigation into the Use of Image Captioning for Automated Software Documentation. In 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER). 514–525. https://doi.org/10.1109/SANER53432.2022.00069Google ScholarGoogle ScholarCross RefCross Ref
  30. Jeffrey Nichols, Brad A. Myers, Michael Higgins, Joseph Hughes, Thomas K. Harris, Roni Rosenfeld, and Mathilde Pignol. 2002. Generating Remote Control Interfaces for Complex Appliances. In Proceedings of the 15th Annual ACM Symposium on User Interface Software and Technology (Paris, France) (UIST ’02). Association for Computing Machinery, New York, NY, USA, 161–170. https://doi.org/10.1145/571985.572008Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Ritam Jyoti Sarmah, Yunpeng Ding, Di Wang, Cheuk Yin Phipson Lee, Toby Jia-Jun Li, and Xiang ’Anthony’ Chen. 2020. Geno: A Developer Tool for Authoring Multimodal Interaction on Existing Web Applications. In Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology (Virtual Event, USA) (UIST ’20). Association for Computing Machinery, New York, NY, USA, 1169–1181. https://doi.org/10.1145/3379337.3415848Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Alborz Rezazadeh Sereshkeh, Gary Leung, Krish Perumal, Caleb Phillips, Minfan Zhang, Afsaneh Fazly, and Iqbal Mohomed. 2020. VASTA: A Vision and Language-Assisted Smartphone Task Automation System. In Proceedings of the 25th International Conference on Intelligent User Interfaces (Cagliari, Italy) (IUI ’20). Association for Computing Machinery, New York, NY, USA, 22–32. https://doi.org/10.1145/3377325.3377515Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Wolfgang Stuerzlinger, Olivier Chapuis, Dusty Phillips, and Nicolas Roussel. 2006. User Interface Façades: Towards Fully Adaptable User Interfaces. UIST ’06: ACM Symposium on User Interface Software and Technology (10 2006). https://doi.org/10.1145/1166253.1166301Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Amanda Swearngin, Amy J. Ko, and James Fogarty. 2017. Genie: Input Retargeting on the Web through Command Reverse Engineering. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 4703–4714. https://doi.org/10.1145/3025453.3025506Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Amanda Swearngin, Chenglong Wang, Alannah Oleson, James Fogarty, and Amy J. Ko. 2020. Scout: Rapid Exploration of Interface Layout Alternatives through High-Level Design Constraints. Association for Computing Machinery, New York, NY, USA, 1–13. https://doi.org/10.1145/3313831.3376593Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Pedro Szekely, Ping Luo, and Robert Neches. 1993. Beyond Interface Builders: Model-Based Interface Tools. In Proceedings of the INTERCHI ’93 Conference on Human Factors in Computing Systems (Amsterdam, The Netherlands) (INTERCHI ’93). IOS Press, NLD, 383–390.Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Bryan Wang, Gang Li, Xin Zhou, Zhourong Chen, Tovi Grossman, and Yang Li. 2021. Screen2Words: Automatic Mobile UI Summarization with Multimodal Learning. (2021).Google ScholarGoogle Scholar
  38. Brad Vander Zanden and Brad A. Myers. 1990. Automatic, Look-and-Feel Independent Dialog Creation for Graphical User Interfaces. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Seattle, Washington, USA) (CHI ’90). Association for Computing Machinery, New York, NY, USA, 27–34. https://doi.org/10.1145/97243.97248Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Xiaoyi Zhang, Lilian de Greef, Amanda Swearngin, Samuel White, Kyle Murray, Lisa Yu, Qi Shan, Jeffrey Nichols, Jason Wu, Chris Fleizach, Aaron Everitt, and Jeffrey P Bigham. 2021. Screen Recognition: Creating Accessibility Metadata for Mobile Applications from Pixels. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3411764.3445186Google ScholarGoogle ScholarDigital LibraryDigital Library

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    • Published in

      cover image ACM Conferences
      CHI EA '23: Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems
      April 2023
      3914 pages
      ISBN:9781450394222
      DOI:10.1145/3544549

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      • Published: 19 April 2023

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