Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/99990
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Title: Detecting resource utilization bugs induced by variant lifecycles in Android
Authors: Lu, Y
Pan, M
Pei, Y 
Li, X
Issue Date: 2022
Source: In Ryu, S. & Smaragdakis, Y. (Eds.). ISSTA 2022: Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis, p. 642-653. New York, NY: Association for Computing Machinery, 2022.
Abstract: The lifecycle models of Android components such as Activities and Fragments predefine the possible orders in which the components' callback methods will be invoked during app executions. Correspondingly, resource utilization operations performed by Android components must comply with all possible lifecycles to ensure safe utilization of the resources in all circumstances, which, however, can be challenging to achieve. In response to the challenge, various techniques have been developed to detect resource utilization bugs that manifest themselves when components go through common lifecycles, but the fact that Android components may execute their callback methods in uncommon orders, leading to variant component lifecycles, has largely been overlooked by the existing techniques. In this paper, we first identify three variant lifecycles for Android Activities and Fragments and then develop a technique called VALA to automatically detect bugs in Android apps that are induced by the variant lifecycles and may cause resource utilization errors like resource leaks and data losses. In an experimental evaluation conducted on 35 Android apps, a supporting tool for the VALA technique automatically detected 8 resource utilization bugs. All the 8 bugs were manually confirmed to be real defects and 7 of them were reported for the first time.
Keywords: Android applications
Variant lifecycles
Resource utilization bugs
Static analysis
Publisher: Association for Computing Machinery, Inc.
ISBN: 978-1-4503-9379-9
DOI: 10.1145/3533767.3534413
Description: ISSTA 2022
Rights: © Association for Computing Machinery 2022. 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 ISSTA 2022 : proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis, https://doi.org/10.1145/3533767.3534413.
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