Testing tools for Android context-aware applications: a systematic mapping

被引:10
作者
Almeida D.R. [1 ]
Machado P.D.L. [1 ]
Andrade W.L. [1 ]
机构
[1] Federal University of Campina Grande - UFCG, Aprigio Veloso Street, 882, Campina Grande
关键词
Android; Context-aware application; Testing automation;
D O I
10.1186/s13173-019-0093-7
中图分类号
学科分类号
摘要
Context: Mobile devices, such as smartphones, have increased their capacity of information processing and sensors have been aggregated to their hardware. Such sensors allow capturing information from the environment in which they are introduced. As a result, mobile applications that use the environment and user information to provide services or perform context-based actions are increasingly common. This type of application is known as context-aware application. While software testing is an expensive activity in general, testing context-aware applications is an even more expensive and challenging activity. Thus, efforts are needed to automate testing for context-aware applications, particularly in the scope of Android, which is currently the most used operating system by smartphones. Objective: This paper aims to identify and discuss the state-of-the-art tools that allow the automation of testing Android context-aware applications. Method: In order to do so, we carried out a systematic mapping study (SMS) to find out the studies in the existing literature that describe or present Android testing tools. The discovered tools were then analyzed to identify their potential in testing Android context-aware applications. Result: A total of 68 works and 80 tools were obtained as a result of the SMS. From the identified tools, five are context-aware Android application testing tools, and five are general Android application testing tools, but support the test of the context-aware feature. Conclusion: Although context-aware application testing tools do exist, they do not support automatic generation or execution of test cases focusing on high-level contexts. Moreover, they do not support asynchronous context variations. © 2019, The Author(s).
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共 114 条
  • [101] Koroglu Y., Sen A., TCM: Test Case Mutation to Improve Crash Detection in Android, Fundamental Approaches to Software Engineering, pp. 264-280, (2018)
  • [102] (2019)
  • [103] Azim T., Neamtiu I., Targeted and depth-first exploration for systematic testing of android apps, SIGPLAN Not, 48, 10, pp. 641-660, (2013)
  • [104] Zeng X., Li D., Zheng W., Xia F., Deng Y., Lam W., Yang W., Xie T., Automated test input generation for android: are we really there yet in an industrial case?, Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering–FSE 2016, pp. 987-992, (2016)
  • [105] Amalfitano D., Fasolino A.R., Tramontana P., Ta B.D., Memon A.M., MobiGUITAR: Automated Model-Based Testing of Mobile Apps, IEEE Softw, 32, 5, pp. 53-59, (2015)
  • [106] Linares-Vasquez M., Bernal-Cardenas C., Moran K., Poshyvanyk D., How do developers test android applications?, In: 2017 IEEE International Conference on Software Maintenance and Evolution (ICSME), pp. 613-622, (2017)
  • [107] Villanes I.K., Ascate S.M., Gomes J., Dias-Neto A.C., What are software engineers asking about android testing on stack overflow?, Proceedings of the 31st Brazilian Symposium on Software Engineering–SBES’17, pp. 104-113, (2017)
  • [108] Helppi V.-V., Calabash 101 - Basics, Getting Started, and Advanced Tips, (2016)
  • [109] Automated UI Testing with Cucumber and Calabash, (2019)
  • [110] Mirza A.M., Khan M.N.A., An automated functional testing framework for context-aware applications, IEEE Access, 6, pp. 46568-46583, (2018)