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Insights into layout patterns of mobile user interfaces by an automatic analysis of android apps

Published: 24 June 2013 Publication History

Abstract

Mobile phones recently evolved into smartphones that provide a wide range of services. One aspect that differentiates smartphones from their predecessor is the app model. Users can easily install third party applications from central mobile application stores. In this paper we present a process to gain insights into mobile user interfaces on a large scale. Using the developed process we automatically disassemble and analyze the 400 most popular free Android applications. The results suggest that the complexity of the user interface differs between application categories. Further, we analyze interface layouts to determine the most frequent interface elements and identify combinations of interface widgets. The most common combination that consists of three nested elements covers 5.43% of all interface elements. It is more frequent than progress bars and checkboxes. The ten most frequent patterns together cover 21.13% of all interface elements. They are all more frequent than common widget including radio buttons and spinner. We argue that the combinations identified not only provide insights about current mobile interfaces, but also enable the development of new optimized widgets.

References

[1]
Android Developer Guideline, accessed 17.12.2012. http://developer.android.com/guide/practices/ui_guidelines/index.html.
[2]
apktool Software, accessed 17.12.2012. http://code.google.com/p/android-apktool/.
[3]
Google Blog, accessed 17.12.2012. http://officialandroid.blogspot.de/2012/09/google-play-hits-25-billion-downloads.html.
[4]
smali - An assembler/disassembler for Android's dex format, accessed 17.12.2012. http://code.google.com/p/smali/.
[5]
Amalfitano, D., Fasolino, A., Tramontana, P., De Carmine, S., and Memon, A. Using gui ripping for automated testing of android applications. In Proc. ASE (2012).
[6]
Balagtas-Fernandez, F., Forrai, J., and Hussmann, H. Evaluation of user interface design and input methods for applications on mobile touch screen devices. In Proc. Interact (2009).
[7]
Böhmer, M., Hecht, B., Schöning, J., Krüger, A., and Bauer, G. Falling asleep with angry birds, facebook and kindle: a large scale study on mobile application usage. In Proc. MobileHCI (2011).
[8]
Cui, Y., and Roto, V. How people use the web on mobile devices. In Proc. WWW (2008).
[9]
Enck, W., Ongtang, M., and McDaniel, P. On lightweight mobile phone application certification. In Proc. CCS (2009).
[10]
Gibler, C., Crussell, J., Erickson, J., and Chen, H. Androidleaks: Automatically detecting potential privacy leaks in android applications on a large scale. Trust and Trustworthy Computing (2012).
[11]
Gilbert, P., Chun, B., Cox, L., and Jung, J. Vision. automated security validation of mobile apps at app markets. In Proc. MCS (2011).
[12]
Henze, N., Pielot, M., Poppinga, B., Schinke, T., and Boll, S. My app is an experiment: Experience from user studies in mobile app stores. IJMHCI (2011).
[13]
Henze, N., Rukzio, E., and Boll, S. 100,000,000 taps: analysis and improvement of touch performance in the large. In Proc. MobileHCI (2011).
[14]
Henze, N., Rukzio, E., and Boll, S. Observational and experimental investigation of typing behaviour using virtual keyboards on mobile devices. In Proc. CHI (2012).
[15]
Hrabia, C.-E., Wolf, K., and Wilhelm, M. Whole hand modeling using 8 wearable sensors: biomechanics for hand pose prediction. In Proc. AH (2013).
[16]
Hu, C., and Neamtiu, I. A gui bug finding framework for android applications. In Proc. SAC (2011).
[17]
17. Leiva, L., Böhmer, M., Gehring, S., et al. Back to the app: The costs of mobile application interruptions. In Proc. MobileHCI (2012).
[18]
Lim, S. L., and Bentley, P. J. How to be a successful app developer: lessons from the simulation of an app ecosystem. In Proc. GECCO (2012).
[19]
Mahmood, R., Esfahani, N., Kacem, T., Mirzaei, N., Malek, S., and Stavrou, A. A whitebox approach for automated security testing of android applications on the cloud. In Proc. AST (2012).
[20]
Möller, A., Diewald, S., Roalter, L., Michahelles, F., and Kranz, M. Update behavior in app markets and security implications: A case study in google play. In Proc. MobileHCI (2012).
[21]
Nielsen. Smartphones Account for Half of all Mobile Phones, Dominate New Phone Purchases in the US, accessed 17.12.2012. http://blog.nielsen.com/nielsenwire/online_mobile/smartphones-accountfor-half-of-all-mobile-phones-dominate-newphone-purchases-in-the-us/.
[22]
Rahmati, A., and Zhong, L. Studying smartphone usage: Lessons from a four-month field study. IEEE Transactions on Mobile Computing (2012).
[23]
Raneburger, D., Popp, R., and Vanderdonckt, J. An automated layout approach for model-driven wimp-ui generation. In Proc. EICS (2012).
[24]
Sahami Shirazi, A., Rohs, M., Schleicher, R., Kratz, S., Mòller, A., and Schmidt, A. Real-time nonverbal opinion sharing through mobile phones during sports events. In Proc. CHI (2011).
[25]
Saylor, M. The Mobile Wave: How Mobile Intelligence Will Change Everything. Vanguard, 2012.
[26]
Schleicher, R., Sahami Shirazi, A., Rohs, M., Kratz, S., and Schmidt, A. Worldcupinion experiences with an android app for real-time opinion sharing during soccer world cup games. IJMHCI (2011).
[27]
Shabtai, A., Kanonov, U., Elovici, Y., Glezer, C., and Weiss, Y. andromaly: a behavioral malware detection framework for android devices. Journal of Intelligent Information Systems (2012).
[28]
Silva, C. E. Reverse engineering of gwt applications. In Proc. EICS (2012).
[29]
Silva, J. L., Campos, J., and Harrison, M. Formal analysis of ubiquitous computing environments through the apex framework. In Proc. EICS (2012).
[30]
Szydlowski, M., Egele, M., Kruegel, C., and Vigna, G. Challenges for dynamic analysis of ios applications. Open Problems in Network Security (2012).
[31]
Verkasalo, H. Contextual patterns in mobile service usage. Personal and Ubiquitous Computing 13, 5 (2009).
[32]
Zhang, S., Lü, H., and Ernst, M. D. Finding errors in multithreaded gui applications. In Proc. ISSTA (2012).
[33]
Zheng, C., Zhu, S., Dai, S., Gu, G., Gong, X., Han, X., and Zou, W. Smartdroid: an automatic system for revealing ui-based trigger conditions in android applications. In Proc. SPSM (2012).

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    cover image ACM Conferences
    EICS '13: Proceedings of the 5th ACM SIGCHI symposium on Engineering interactive computing systems
    June 2013
    356 pages
    ISBN:9781450321389
    DOI:10.1145/2494603
    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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    Published: 24 June 2013

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    Author Tags

    1. android
    2. apps
    3. design
    4. mobile applications
    5. pattern
    6. reverse engineering
    7. user interface
    8. widget

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    • (2024)Automatic Macro Mining from Interaction Traces at ScaleProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642074(1-16)Online publication date: 11-May-2024
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    • (2023)An Empirical Study on Multimodal Activity Clustering of Android ApplicationsIEEE Access10.1109/ACCESS.2023.328098511(53598-53614)Online publication date: 2023
    • (2023)Methods for Automatic Web Page Layout Testing and Analysis: A ReviewIEEE Access10.1109/ACCESS.2023.324254911(13948-13964)Online publication date: 2023
    • (2023)Deep Learning-Based Model Using DensNet201 for Mobile User Interface EvaluationInternational Journal of Human–Computer Interaction10.1080/10447318.2023.217549439:9(1981-1994)Online publication date: 12-Feb-2023
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