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

Author:

Almeida Diego R.ORCID,Machado Patrícia D. L.,Andrade Wilkerson L.

Abstract

Abstract 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.

Publisher

Springer Science and Business Media LLC

Subject

General Computer Science

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1. Test Case Generation Approach for Android Applications using Reinforcement Learning;Engineering, Technology & Applied Science Research;2024-08-02

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3. DroidSecTester: Towards context-driven modelling and detection of Android application vulnerabilities;2023 IEEE 34th International Symposium on Software Reliability Engineering Workshops (ISSREW);2023-10-09

4. Smartphone Context Event Sequence Prediction with POERMH and TKE-Rules Algorithms;2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC);2023-03-08

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