Extending Aspect-Oriented Programming for Dynamic User's Activity Detection in Mobile App Analytics

Abstract

Mobile apps analytics represent a core set in the mobile industry to extract relevant data with the aim of modeling user's behavior. Current solutions to detect in-app user's activity are usually based on a continuous app code modification schema, which implies high development efforts and a clear problem to implement changes without compromising the time to come back to the market or even with dependencies in the user's app updates. In this article, we analyze the suitability of aspect-oriented programming for providing a more efficient way to detect user's activity inside apps, which may lead to obtain user analytics. We propose an innovative approach that relies on an in-app solution based on the embedding of a specific library and a configuration file for setting up the events to be tracked in real time, without additional code changes in the app. Thus, this new schema will reduce the time and effort costs derived from the integration of third party trackers.