Luca Ardito, Riccardo Coppola, Simone Leonardi, Maurizio Morisio, and Ugo Buy. 2020. Automated test selection for Android apps based on APK and activity classification. IEEE Access 8 (2020), 187648–187670.
Jane Bromley, Isabelle Guyon, Yann LeCun, Eduard Säckinger, and Roopak Shah. 1993. Signature verification using a" siamese" time delay neural network. Advances in neural information processing systems 6 (1993), 737–744.
Kyunghyun Cho, Bart van Merriënboer, Dzmitry Bahdanau, and Yoshua Bengio. 2014. On the Properties of Neural Machine Translation: Encoder–Decoder Approaches. In Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation. 103–111.
Li Deng and Dong Yu. 2014. Deep learning: methods and applications. Foundations and trends in signal processing 7, 3-4 (2014), 197–387.
Matthias Fey and Jan Eric Lenssen. 2019. Fast graph representation learning with PyTorch Geometric. In Proceedings of the 7th International Conference on Learning Representations, RLGM Workshop (ICLR’19).
JS Foundation. 2022. Appium: Mobile App Automation Made Awesome. http://appium.io/. Online; accessed 15-Feb-2022.
William L Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS’17). MIT Press, 1025–1035.
Gang Hu, Linjie Zhu, and Junfeng Yang. 2018. AppFlow: using machine learning to synthesize robust, reusable UI tests. In Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE’18). ACM, 269–282.
Diederik P Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In Proceedings of the 3th International Conference on Learning Representations (ICLR’15).
Thomas N Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In Proceedings of the 5th International Conference on Learning Representations (ICLR’17).
Yujia Li, Richard Zemel, Marc Brockschmidt, and Daniel Tarlow. 2016. Gated graph sequence neural networks. In Proceedings of the 4th International Conference on Learning Representations (ICLR’16).
Minxue Pan, An Huang, Guoxin Wang, Tian Zhang, and Xuandong Li. 2020. Reinforcement learning based curiosity-driven testing of Android applications. In Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA’20). ACM, 153–164.
Minxue Pan, Tongtong Xu, Yu Pei, Zhong Li, Tian Zhang, and Xuandong Li. 2020. GUI-Guided Test Script Repair for Mobile Apps. IEEE Transactions on Software Engineering (2020), 1–1.
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. 2017. Automatic differentiation in pytorch. In Proceedings of the 31st International Conference on Neural Information Processing Systems,Workshop (NIPS’17). MIT Press.
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2018. Graph attention networks. In Proceedings of the 6th International Conference on Learning Representations (ICLR’18).
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S. Yu. 2021. A Comprehensive Survey on Graph Neural Networks. IEEE Transactions on Neural Networks and Learning Systems 32, 1 (2021), 4–24.
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How powerful are graph neural networks?. In Proceedings of the 7th International Conference on Learning Representations (ICLR’19).
Tongtong Xu, Minxue Pan, Yu Pei, Guiyin Li, Xia Zeng, Tian Zhang, Yuetang Deng, and Xuandong Li. 2021. GUIDER: GUI structure and vision co-guided test script repair for Android apps. In Proceedings of the 30th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA’21). ACM, 191–203.
Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun. 2020. Graph neural networks: A review of methods and applications. AI Open 1 (2020), 57–81.
