skip to main content
10.1145/2993259.2993265acmconferencesArticle/Chapter ViewAbstractPublication PagesfseConference Proceedingsconference-collections
research-article

Checking app user interfaces against app descriptions

Published:14 November 2016Publication History

ABSTRACT

Does the advertised behavior of apps correlate with what a user sees on a screen? In this paper, we introduce a technique to statically extract the text from the user interface definitions of an Android app. We use this technique to compare the natural language topics of an app’s user interface against the topics from its app store description. A mismatch indicates that some feature is exposed by the user interface, but is not present in the description, or vice versa. The popular Twitter app, for instance, spots UI elements that al- low to make purchases; however, this feature is not mentioned in its description. Likewise, we identified a number of apps whose user interface asks users to access or supply sensitive data; but this “feature” is not mentioned in the description. In the long run, analyzing user interface topics and comparing them against external descriptions opens the way for checking general mismatches between requirements and implementation.

References

  1. A. A. Al-Subaihin, F. Sarro, S. Black, L. Capra, M. Harman, Y. Jia, and Y. Zhang. Clustering mobile apps based on mined textual descriptions. In Proceedings of the 10th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), ESEM ’16, 2016. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. V. Avdiienko, K. Kuznetsov, P. Calciati, J. C. C. Román, A. Gorla, and A. Zeller. CALAPPA: a toolchain for mining android applications. In Proceedings of the 1st International Workshop on App Market Analytics, WAMA 2016, pages –. ACM, 11 2016. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. A. Gorla, I. Tavecchia, F. Gross, and A. Zeller. Checking app behavior against app descriptions. In Proceedings of the 36th International Conference on Software Engineering, ICSE 2014, pages 1025–1035, New York, NY, USA, 2014. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. J. Huang, X. Zhang, L. Tan, P. Wang, and B. Liang. AsDroid: detecting stealthy behaviors in Android applications by user interface and program behavior contradiction. In Proceedings of the 36th International Conference on Software Engineering, ICSE 2014, pages 1036–1046, New York, NY, USA, 2014. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. K. Kuznetsov, A. Gorla, I. Tavecchia, F. Gross, and A. Zeller. Mining android apps for anomalies. In The Art and Science of Analyzing Software Data, pages 257–281. Morgan Kaufmann, 4 2015.Google ScholarGoogle Scholar
  6. R. T.-W. Lo, B. He, and I. Ounis. Automatically building a stopword list for an information retrieval system. In Information Retrieval Workshop, page 17. Citeseer, 2005.Google ScholarGoogle Scholar
  7. A. K. McCallum. Mallet: A machine learning for language toolkit. http://mallet.cs.umass.edu, 2002.Google ScholarGoogle Scholar
  8. S. Nakatani. Language detection library for Java, 2010.Google ScholarGoogle Scholar
  9. R. Pandita, X. Xiao, W. Yang, W. Enck, and T. Xie. WHYPER: Towards automating risk assessment of mobile applications. In USENIX Security Symposium, pages 527–542, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Z. Qu, V. Rastogi, X. Zhang, Y. Chen, T. Zhu, and Z. Chen. AutoCog: Measuring the description-to-permission fidelity in Android applications. In Proceedings of the 21st Conference on Computer and Communications Security (CCS), 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. L. Yu, X. Luo, C. Qian, and S. Wang. Revisiting the description-to-behavior fidelity in android applications. In 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER), volume 1, pages 415–426, March 2016.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. Checking app user interfaces against app descriptions

          Recommendations

          Comments

          Login options

          Check if you have access through your login credentials or your institution to get full access on this article.

          Sign in
          • Published in

            cover image ACM Conferences
            WAMA 2016: Proceedings of the International Workshop on App Market Analytics
            November 2016
            56 pages
            ISBN:9781450343985
            DOI:10.1145/2993259

            Copyright © 2016 ACM

            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]

            Publisher

            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 14 November 2016

            Permissions

            Request permissions about this article.

            Request Permissions

            Check for updates

            Qualifiers

            • research-article

            Upcoming Conference

            FSE '24

          PDF Format

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader