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Detecting Antipatterns in Android Apps

Abstract : Mobile apps are becoming complex software systems that must be developed quickly and evolve continuously to fit new user requirements and execution contexts. However, addressing these constraints may result in poor design choices, known as antipatterns, which may incidentally degrade software quality and performance. Thus, the automatic detection of antipatterns is an important activity that eases both maintenance and evolution tasks. Moreover, it guides developers to refactor their applications and thus, to improve their quality. While antipatterns are well-known in object-oriented applications, their study in mobile applications is still in their infancy. In this paper, we propose a tooled approach, called Paprika, to analyze Android applications and to detect object-oriented and Android-specific antipatterns from binaries of mobile apps. We validate the effectiveness of our approach on a set of popular mobile apps downloaded from the Google Play Store.
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Contributor : Geoffrey Hecht <>
Submitted on : Friday, March 6, 2015 - 5:23:24 PM
Last modification on : Saturday, December 12, 2020 - 6:08:04 PM
Long-term archiving on: : Sunday, June 7, 2015 - 5:35:24 PM


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  • HAL Id : hal-01122754, version 2


Geoffrey Hecht, Romain Rouvoy, Naouel Moha, Laurence Duchien. Detecting Antipatterns in Android Apps. [Research Report] RR-8693, INRIA Lille; INRIA. 2015. ⟨hal-01122754v2⟩



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