The size of Android applications is getting larger to fulfill the requirements of various users. However, not all the features of the applications are needed and desired by a specific user. The unnecessary and non-desired features can increase the attack surface and consume system resources such as storage and memory. To address this issue, we propose a framework, MiniMon, to debloat unnecessary features from an Android app based on the logs of specific users’ interactions with the app.

However, rarely used features may not be recorded during the data collection, and users’ preferences may change slightly over time. To address these challenges, we embed several solutions in our framework that can uncover user-desired features by learning and generalizing from the logs of how users interact with an application. MiniMon first collects the application methods that are executed when users interact with it. Then, given the collected executed methods and the call graph of the application, MiniMon applies 10 techniques to generalize from logs. These include three program analysis-based techniques, two graph clustering-based techniques, and five graph embedding-based techniques to identify the additional methods in an app that are similar to the logged executed methods. Finally, MiniMon generates a debloated application by removing methods that are not similar to the executed methods. To evaluate the performance of variants of MiniMon that use different generalization techniques, we create a benchmark for a controlled experiment. The results show that the graph embedding-based generalization technique that considers the information of all nodes in the call graph is the best, and can correctly uncover 75.5% of the unobserved but desired behaviors and still debloat more than half of the app. We also conducted a user study that uncovers that the use of the intelligent (generalization) method of MiniMon boosts the overall user satisfaction rate by 37.6%.