Random GUI testing of android application using behavioral model

Woramet Muangsiri, Shingo Takada

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Automated GUI testing based on behavioral model is one of the most efficient testing approaches. By mining user usage, test scenarios can be generated based on statistical models such as Markov chain. However, these works require static analysis before starting the exploration which requires too much prerequisites and time. In this work, we propose a behavioralbased GUI testing approach for mobile applications that achieves faster and higher coverage. Our approach does not conduct static analysis. It creates a behavioral model from usage logs by applying a statistical model. The events within the behavioral model is mapped to GUI components in a GUI tree. Finally, it updates the model dynamically to increase the probability of an event that rarely or never occurs when users use the application. We evaluated our approach on three open-source Android applications, and compared it with other approaches. Our approach showed the effectiveness of our tool.

Original languageEnglish
Title of host publicationProceedings - SEKE 2017
Subtitle of host publication29th International Conference on Software Engineering and Knowledge Engineering
PublisherKnowledge Systems Institute Graduate School
Pages266-271
Number of pages6
ISBN (Electronic)1891706411
DOIs
Publication statusPublished - 2017
Event29th International Conference on Software Engineering and Knowledge Engineering, SEKE 2017 - Pittsburgh, United States
Duration: 2017 Jul 52017 Jul 7

Other

Other29th International Conference on Software Engineering and Knowledge Engineering, SEKE 2017
CountryUnited States
CityPittsburgh
Period17/7/517/7/7

Keywords

  • Android
  • Behavioral model
  • GUI testing
  • Testing automation
  • Testing tools

ASJC Scopus subject areas

  • Software

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  • Cite this

    Muangsiri, W., & Takada, S. (2017). Random GUI testing of android application using behavioral model. In Proceedings - SEKE 2017: 29th International Conference on Software Engineering and Knowledge Engineering (pp. 266-271). Knowledge Systems Institute Graduate School. https://doi.org/10.18293/SEKE2017-099