Mining Test Repositories for Automatic Detection of UI Performance Regressions in Android AppsReport as inadecuate

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1 SPIRALS - Self-adaptation for distributed services and large software systems Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 2 Université de Lille, Sciences et Technologies 3 EPM - Ecole Polytechnique de Montreal 4 IUF - Institut Universitaire de France

Abstract : The reputation of a mobile app vendor’s apps is crucial to survive amongst the ever increasing competition, however this reputation largely depends on the quality of the apps, both functional and non-functional. One major non-functional requirement of mobile apps is to guarantee smooth UI interactions, since choppy scrolling or navigation caused by performance problems on a mobile device’s limited hardware resources is highly annoying for end-users. The main research challenge of automatically identifying UI performance problems on mobile devices is that the performance of an app highly varies depending on its context—i.e., the hardware and software configurations on which it runs.This paper presents DUNE, an approach to automatically detect UI performance degradations in Android apps while taking into account context differences. DUNE builds an ensemble model of the UI performance of historical test runs that are known to be acceptable, for different configurations of context. We empirically evaluate DUNE on real UI performance defects reported in two Android apps. We demonstrate that this toolset can be successfully used to spot UI performance regressions at a fine granularity.

Keywords : Android context performance regression mining

Author: Maria Gomez - Romain Rouvoy - Bram Adams - Lionel Seinturier -



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