Muccini, H., Di Francesco, A., Esposito, P.: Software testing of mobile applications: challenges and future research directions.Proceedings of the 28th International Conference on Software Engineering & Knowledge Engineering (SEKE 2016), Redwood City, CA, USA, July 1-3 (2016). F., Vilkomir, S.: Practical combinatorial testing approaches: a case study of a university portal application. N.: Practical combinatorial testing-beyond pairwise. N., Lei, Y.: Introduction to combinatorial testing, Chapman and Hall/CRC, 341 pages (2013). Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud), Oxford, UK, 8-11 April, pp. Huang, J.: AppACTS: mobile app automated compatibility testing service.Proceedings of the 19th Working Conference on Reverse Engineering (WCRE 2012), Kingston, ON, Canada, October 15-18, pp.
Ghostlab software android#
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Software Testing, Verification and Reliability, 15(3), 167-199. Combination testing strategies: a survey. Duggan M.: Cell phone activities 2013 (2013).Cutler K.: How do top Android developers QA test their apps? (2012).Barksdale M.: Mobile platform fragmentation-what it is and how we deal with it at Engage Mobile (2014).Our results include recommendations for increasing the effectiveness while decreasing the costs of mobile testing. The most successful approaches were the coverage of different types of Android operating systems and the each-choice coverage. However, coverage of device characteristics in the selection process yielded an acceptable 90% level of effectiveness with a set of only five devices. Our research shows that a random selection of 13 devices achieved 100% effectiveness. To collect the experimental data, 15 Android applications were tested on 30 mobile devices and 24 device-specific faults were detected. We experimentally investigated a simple coverage of all values of each device's features separately and the each-choice coverage (i.e., the coverage of all device characteristics at the same time). The goal of this research was to determine how many devices must be tested and which methods for device selection are best for revealing device-specific faults. For this reason, multi-device testing is necessary. Due to the large number of such devices on the market and the variations in their characteristics, it is hard to guarantee that an application will work as intended on all devices. This paper evaluates the effectiveness of coverage approaches for selecting mobile devices (i.e., smartphones and tablets) to test mobile software applications.
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