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ASE 2021
Sun 14 - Sat 20 November 2021 Australia
Thu 18 Nov 2021 21:00 - 21:20 at Kangaroo - Learning Applications Chair(s): Michael Pradel

Despite the proliferation of Android testing tools, Google Monkey has remained the de facto standard for practitioners. The popularity of Google Monkey is largely due to the fact that it is a black-box testing tool, making it widely applicable to all types of Android apps, regardless of their underlying implementation details. An important drawback of Google Monkey, however, is the fact that it uses the most naive form of test input generation technique, i.e., random testing.In this work, we present Deep GUI, an approach that aims to complement the benefits of black-box testing with a more intelligent form of GUI input generation. Given only screenshots of apps, Deep GUI first employs deep learning to construct a model of valid GUI interactions. It then uses this model to generate effective inputs for an app under test without the need to probe its implementation details. Moreover, since the data collection, training, and inference processes are performed independent of the platform, the model inferred by Deep GUI has application for testing apps in other platforms as well. We implemented a prototype of Deep GUI in a tool called Monkey++ by extending Google Monkey and evaluated it for its ability to crawl Android apps. We found that Monkey++ achieves significant improvements over Google Monkey in cases where an app’s UI is complex, requiring sophisticated inputs. Furthermore, our experimental results demonstrate the model inferred using Deep GUI can be reused for effective GUI input generation across platforms without the need for retraining.

Thu 18 Nov

Displayed time zone: Hobart change

21:00 - 22:00
Learning ApplicationsResearch Papers / Tool Demonstrations / Journal-first Papers at Kangaroo
Chair(s): Michael Pradel University of Stuttgart
Deep GUI: Black-box GUI Input Generation with Deep Learning
Research Papers
Faraz YazdaniBanafsheDaragh University of California, Irvine, Sam Malek University of California at Irvine, USA
Towards Exploring the Limitations of Active Learning: An Empirical Study
Research Papers
Qiang Hu University of Luxembourg, Yuejun GUo University of Luxembourg, Maxime Cordy University of Luxembourg, Luxembourg, Xiaofei Xie Kyushu University, Wei Ma University of Luxembourg, Mike Papadakis University of Luxembourg, Luxembourg, Yves Le Traon University of Luxembourg, Luxembourg
Machine Learning based Success Prediction for Crowdsourcing Software Projects
Journal-first Papers
Inam Illahi Beijing Institute of Technology, Hui Liu Beijing Institute of Technology, Qasim Umer Beijing Institute of Technology, Nan Niu University of Cincinnati
SoManyConflicts: Resolve Many Merge Conflicts Interactively and Systematically
Tool Demonstrations
Bo Shen Peking University, Wei Zhang Peking University, Ailun Yu Peking University, Yifan Shi Peking University, Haiyan Zhao Peking University, Zhi Jin Peking University