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http://dx.doi.org/10.7471/ikeee.2019.23.2.448

GUI-based Detection of Usage-state Changes in Mobile Apps  

Kang, Ryangkyung (Dept. of Computer and Communication Engineering, Kangwon National University)
Seok, Ho-Sik (Dept. of Computer and Communication Engineering, Kangwon National University)
Publication Information
Journal of IKEEE / v.23, no.2, 2019 , pp. 448-453 More about this Journal
Abstract
Under the conflicting objectives of maximum user satisfaction and fast launching, there exist great needs for automated mobile app testing. In automated app testing, detection of usage-state changes is one of the most important issues for minimizing human intervention and testing of various usage scenarios. Because conventional approaches utilizing pre-collected training examples can not handle the rapid evolution of apps, we propose a novel method detecting changes in usage-state through graph-entropy. In the proposed method, widgets in a screen shot are recognized through DNNs and 'onverted graphs. We compared the performance of the proposed method with a SIFT (Scale-Invariant Feature Transform) based method on 20 real-world apps. In most cases, our method achieved superior results, but we found some situations where further improvements are required.
Keywords
Automated app testing; Deep neural nets; Graph entropy; Stream analysis; Change detection;
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1 Y. Li, Z. Yang, Y. Guo, and X. Chen, "A Deep Learning based Approach to Automated Android App Testing," arXiv:1901.02633, 2019.
2 D. Grattarola, D. Zambon, C. Alippi, and L. Livi, "Change Detection in Graph Streams by Learning Graph Embeddings on Constant-Curvature Manifolds," arXiv:1805.06299v3, 2019.
3 K. Janos, "Coding of an information source having ambiguous alphabet and the entropy of graphs," in Proc. of the 6th Prague conference on information theory, 1973.
4 A. Mehler, A. Lücking, and P. WeiB, "A network model of interpersonel alignment in dialog," Entropy, vol.12, pp.1440-1483, 2010. DOI: 10.3390/e12061440   DOI
5 M. Dehmer, N. Barbarini, K. Varmuza, and A. Graber, "A large scale analysis of information-theoretic network complexity measures using chemical structures," PLoS ONE, vol.4, no.12, e8057, 2009. DOI: 10.1371/journal.pone.0008057   DOI
6 A. Orlitsky and J. R. Roche, "Coding for computing," IEEE Trans. Info. Theory, vol.47, no.3, pp.903-917, 2001. DOI: 10.1109/SFCS.1995.492580   DOI
7 B. Guan, H. Ye, H. Liu, and W. Sethares, "Target image video search based on local features," arXiv:1808.03735v2, 2019.
8 Y. Wang, L. Du, and H. Dai, "Unsupervised SAR image change detection based on SIFT keypoints and region information," IEEE Trans. Geosci. Remote Sens, vol.13, no.7, pp.931-935, 2016. DOI: 10.1109/LGRS.2016.2554606   DOI
9 https://play.google.com/store/apps/top/
10 S. Ren, K. He, R. Girshick, and J. Sun, "Faster R-CNN: towards real-time object detection with region proposal networks," IEEE Trans. Pattern Anal. Mach. Intell., vol.39, no.6, pp.1137-1149, 2016. DOI: 10.1109/TPAMI.2016.2577031   DOI
11 https://networkx.github.io/
12 K. Mao, M. Harman, and Y. Jia, "Sapienz: multi-objective automated testing for Android applications," in Proc. of the 25th International Symposium on Software Testing and Analysis, pp.94-105, 2016. DOI: 10.1145/2931037.2931054
13 AppBrain, "Number of Android apps on Google Play," https://www.appbrain.com/stats/number-of-android-apps/
14 M. Linares-Vasquez, K. Moran, and D. Poshyvanyk, "Continuous, Evolutionary and Large-Scale: A New Perspective for Automated Mobile App Testing," in Proc. of the IEEE International Conference on Software Maintenance and Evolution(ICSME), 2017. DOI: 10.1109/ICSME.2017.27