Using GUI ripping for automated testing of Android applications

By Domenico Amalfitano, A. Fasolino, Porfirio Tramontana, Salvatore De Carmine, Atif M. Memon. 2012 Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering, 2012

548 citations recorded by the source corpus.

Subfields: Information Systems, Software, Signal Processing

View Paper: https://www.semanticscholar.org/paper/6f2c524b383e344809700e811c0d27114eb909c5

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Citations in the corpus

EvoDroid: segmented evolutionary testing of Android apps (2014, methodology): “In a subsequent work [19], they presented an automated Android GUI ripping approach where task lists are used to ﬁre events on the interface to discover and exercise the GUI model.”
Guided GUI testing of android apps with minimal restart and approximate learning (2013, background): “Recently, a number of model-learning based testing techniques targeting Android application [6, 33, 36, 40, 45] have been proposed.”
DL-Droid: Deep learning based android malware detection using real devices (2019, background): “Choudhary et al. (2015) demonstrated that among the input generation tools analysed comparatively in their study (i.e. Monkey Developers (2012) , Dynodroid Machiry et al. (2013) , ACTEve Anand et al. (2012) , A3E Azim and Neamtiu (2013) , GUIRip-per Amalﬁtano et al. (2012) , SwiftHand Choi et al. (2”
A Grey-Box Approach for Automated GUI-Model Generation of Mobile Applications (2013, methodology): “Recognizing this inadequacy, researchers have very recently begun to develop such techniques [9–13].”
Automated Test Input Generation for Android: Are We There Yet? (E) (2015, unknown): “Among those, 50 are from the Dynodroid paper [18], 3 from GUIRipper [1], 5 from ACTEve [3], and 10 from SwiftHand [8].”
Targeted and depth-first exploration for systematic testing of android apps (2013, background): “Android Ripper [9] is a GUI-based static and dynamic testing tool for Android.”
Sapienz: multi-objective automated testing for Android applications (2016, background): “Developers may reject longer sequences as being impractical for debugging and also unlikely to occur in practice; the longer the generated test sequence, the less likely it is to occur in practice.”
DroidBot: A Lightweight UI-Guided Test Input Generator for Android (2017, methodology): “AndroidRipper [7], SwiftHand [9], A(3)E [13] and GUICC [14] are model-based automated test generators, while using different methods to construct the model and generate input based on the model.”
Free for All! Assessing User Data Exposure to Advertising Libraries on Android (2016, unknown): “27% LOC coverage [4].”
Checking app behavior against app descriptions (2014, methodology): “Integrating such techniques in CHABADA, combined with automated test generation [12, 27, 15, 1], would allow to learn normal and abnormal patterns of information flow; this is part of our future work (Section 6).”
Guided, stochastic model-based GUI testing of Android apps (2017, methodology): “This graph usually generates many infeasible event sequences, and reduces the effectiveness of MBT. AndroidRipper [1], MobiGUITAR [2] (an extension of the former), ORBIT [67] and AMOLA [7] use state machines to represent app models.”
Brahmastra: Driving Apps to Test the Security of Third-Party Components (2014, methodology): “Although recent works propose techniques to improve various types of coverages, computed as the fraction of app activities or methods invoked by the Monkey, coverage still remains far from perfect [26, 14, 13, 25, 27].”
*droid (2016, background): “We refer readers to that work for more information on Android test generation [Amalﬁtano et al. 2012; Aviv et al. 2012; Xu et al. 2012; Gomez et al. 2013; Machiry et al. 2013; Hao et al. 2014; Liang et al. 2014; Mahmood et al. 2014; Narain et al. 2014].”