Ex pede Herculem: Augmenting Activity Transition Graph for Apps via Graph Convolution Network

Zhe Liu, Chunyang Chen, Junjie Wang, Yuhui Su, Yuekai Huang, Jun Hu, Qing Wang

Abstract: Mobile apps are indispensable for people's daily life. With the increase of GUI functions, apps have become more complex and diverse. As the Android app is event-driven, Activity Transition Graph (ATG) becomes an important way of app abstract and graphical user interface (GUI) modeling. Although existing works provide static and dynamic analysis to build ATG for applications, the completeness of ATG obtained is poor due to the low coverage of these techniques. To tackle this challenge, we propose a novel approach, ArchiDroid, to automatically augment the ATG via graph convolution network. It models both the semantics of activities and the graph structure of activity transitions to predict the transition between activities based on the seed ATG extracted by static analysis. The evaluation demonstrates that ArchiDroid can achieve 86% precision and 94% recall in predicting the transition between activities for augmenting ATG. We further apply the augmented ATG in two downstream tasks, i.e., guidance in automated GUI testing and assistance in app function design. Results show that the automated GUI testing tool integrated with ArchiDroid achieves 43% more activity coverage and detects 208% more bugs. Besides, ArchiDroid can predict the missing transition with 85% accuracy in real-world apps for assisting the app function design, and an interview case study further demonstrates its usefulness.