On mobile phones, users and developers use apps official marketplaces serving
as repositories of apps. The Google Play Store and Apple Store are the official
marketplaces of Android and Apple products which offer more than a million
apps. Although both repositories offer description of apps, information
concerning performance is not available. Due to the constrained hardware of
mobile devices, users and developers have to meticulously manage the resources
available and they should be given access to performance information about
apps. Even if this information was available, the selection of apps would still
depend on user preferences and it would require a huge cognitive effort to make
optimal decisions. Considering this fact we propose APOA, a recommendation
system which can be implemented in any marketplace for helping users and
developers to compare apps in terms of performance.
APOA uses as input metric values of apps and a set of metrics to optimize. It
solves an optimization problem and it generates optimal sets of apps for
different user's context. We show how APOA works over an Android case study.
Out of 140 apps, we define typical usage scenarios and we collect measurements
of power, CPU, memory, and network usages to demonstrate the benefit of using
APOA.Comment: 18 pages, 8 figure