Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/99989
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Title: Preference-wise testing of Android apps via test amplification
Authors: Pan, M
Lu, Y
Pei, Y 
Zhang, T
Li, X
Issue Date: Jan-2023
Source: ACM transactions on software engineering and methodology, Jan. 2023, v. 32, no. 1, 4, p. 1-37
Abstract: Preferences, the setting options provided by Android, are an essential part of Android apps. Preferences allow users to change app features and behaviors dynamically, and therefore their impacts need to be considered when testing the apps. Unfortunately, few test cases explicitly specify the assignments of valid values to the preferences, or configurations, under which they should be executed, and few existing mobile testing tools take the impact of preferences into account or provide help to testers in identifying and setting up the configurations for running the tests. This article presents the Prefest approach to effective testing of Android apps with preferences. Given an Android app and a set of test cases for the app, Prefest amplifies the test cases with a small number of configurations to exercise more behaviors and detect more bugs that are related to preferences. In an experimental evaluation conducted on real-world Android apps, amplified test cases produced by Prefest from automatically generated test cases covered significantly more code of the apps and detected seven real bugs, and the tool's test amplification time was at the same order of magnitude as the running time of the input test cases. Prefest's effectiveness and efficiency in amplifying programmer-written test cases was comparable with that in amplifying automatically generated test cases.
Keywords: Android apps
Android testing
Preference-wise testing
Publisher: Association for Computing Machinery, Inc.
Journal: ACM transactions on software engineering and methodology 
ISSN: 1049-331X
EISSN: 1557-7392
DOI: 10.1145/3511804
Rights: © Association for Computing Machinery 2023. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Transactions on Software Engineering and Methodology, https://doi.org/10.1145/3511804.
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