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

Authors:

Qing WangAuthors Info & Claims

ICSE '23: Proceedings of the 45th International Conference on Software Engineering

Pages 1983 - 1995

https://doi.org/10.1109/ICSE48619.2023.00168

Published: 26 July 2023 Publication History

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.

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Cited By

Yarifard AAraban SPaydar SGarousi VMorisio MCoppola R(2025)Extraction and empirical evaluation of GUI-level invariants as GUI Oracles in mobile app testingInformation and Software Technology10.1016/j.infsof.2024.107531177:COnline publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1016/j.infsof.2024.107531
Liu ZChen CWang JChen MWu BHuang YHu JWang Q(2024)Unblind Text Inputs: Predicting Hint-text of Text Input in Mobile Apps via LLMProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642939(1-20)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642939
Liu ZChen CWang JChen MWu BChe XWang DWang QRoychoudhury APaiva AAbreu RStorey M(2024)Make LLM a Testing Expert: Bringing Human-like Interaction to Mobile GUI Testing via Functionality-aware DecisionsProceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3639180(1-13)Online publication date: 20-May-2024
https://dl.acm.org/doi/10.1145/3597503.3639180
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ICSE '23: Proceedings of the 45th International Conference on Software Engineering

May 2023

2713 pages

ISBN:9781665457019

General Chair:
John Grundy
Department of Software Systems and Cybersecurity, Faculty of IT, Monash University, Australia
,
Program Co-chairs:
Lori Pollock
University of Delaware, DE, USA
,
Massimiliano Di Penta
University of Sannio, Italy

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Yarifard AAraban SPaydar SGarousi VMorisio MCoppola R(2025)Extraction and empirical evaluation of GUI-level invariants as GUI Oracles in mobile app testingInformation and Software Technology10.1016/j.infsof.2024.107531177:COnline publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1016/j.infsof.2024.107531
Liu ZChen CWang JChen MWu BHuang YHu JWang Q(2024)Unblind Text Inputs: Predicting Hint-text of Text Input in Mobile Apps via LLMProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642939(1-20)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642939
Liu ZChen CWang JChen MWu BChe XWang DWang QRoychoudhury APaiva AAbreu RStorey M(2024)Make LLM a Testing Expert: Bringing Human-like Interaction to Mobile GUI Testing via Functionality-aware DecisionsProceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3639180(1-13)Online publication date: 20-May-2024
https://dl.acm.org/doi/10.1145/3597503.3639180
Liu ZChen CWang JChen MWu BTian ZHuang YHu JWang QRoychoudhury APaiva AAbreu RStorey M(2024)Testing the Limits: Unusual Text Inputs Generation for Mobile App Crash Detection with Large Language ModelProceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3639118(1-12)Online publication date: 20-May-2024
https://dl.acm.org/doi/10.1145/3597503.3639118

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