Extending AspectOriented Programming for Dynamic Users Activity Detection in Mobile App Analytics Mobile apps analytics represent a core set in the mobile industry to extract relevant data with the aim of modeling useru0027s behavior. Current solutions to detect inapp useru0027s 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 useru0027s app updates. In this article, we analyze the suitability of aspectoriented programming for providing a more efficient way to detect useru0027s activity inside apps, which may lead to obtain user analytics. We propose an innovative approach that relies on an inapp 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.

Demystifying Application Performance Management Libraries for Android Since the performance issues of apps can influence usersu0027 experience, developers leverage application performance management (APM) tools to locate the potential performance bottleneck of their apps. Unfortunately, most developers do not understand how APMs monitor their apps during the runtime and whether these APMs have any limitations. In this paper, we demystify APMs by inspecting 25 widelyused APMs that target on Android apps. We first report how these APMs implement 8 key functions as well as their limitations. Then, we conduct a largescale empirical study on 500,000 Android apps from Google Play to explore the usage of APMs. This study has some interesting observations about existing APMs for Android, including 1) some APMs still use deprecated permissions and approaches so that they may not always work properly; 2) some app developers use APMs to collect usersu0027 privacy information.