Model-based conformance testing for android

Yiming Jing, Gail-Joon Ahn, Hongxin Hu

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

9 Scopus citations

Abstract

With the surging computing power and network connectivity of smartphones, more third-party applications and services are deployed on these platforms and enable users to customize their mobile devices. Due to the lack of rigorous security analysis, fast evolving smartphone platforms, however, have suffered from a large number of system vulnerabilities and security flaws. In this paper, we present a model-based conformance testing framework for mobile platforms, focused on Android platform. Our framework systematically generates test cases from the formal specification of the mobile platform and performs conformance testing with the generated test cases. We also demonstrate the feasibility and effectiveness of our framework through case studies on Android Inter-Component Communication module.

Original languageEnglish (US)
Title of host publicationAdvances in Information and Computer Security - 7th International Workshop on Security, IWSEC 2012, Proceedings
Pages1-18
Number of pages18
DOIs
StatePublished - Nov 9 2012
Event7th International Workshop on Security, IWSEC 2012 - Fukuoka, Japan
Duration: Nov 7 2012Nov 9 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7631 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other7th International Workshop on Security, IWSEC 2012
CountryJapan
CityFukuoka
Period11/7/1211/9/12

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Jing, Y., Ahn, G-J., & Hu, H. (2012). Model-based conformance testing for android. In Advances in Information and Computer Security - 7th International Workshop on Security, IWSEC 2012, Proceedings (pp. 1-18). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7631 LNCS). https://doi.org/10.1007/978-3-642-34117-5-1