Automated Test Input Generation for Android: Are We There Yet? (E)

By Shauvik Roy Choudhary, Alessandra Gorla, Alessandro Orso. International Conference on Automated Software Engineering, 2015

489 citations recorded by the source corpus.

Like all software, mobile applications ("apps") must be adequately tested to gain confidence that they behave correctly. Therefore, in recent years, researchers and practitioners alike have begun to investigate ways to automate apps testing. In particular, because of Android's open source nature and its large share of the market, a great deal of research has been performed on input generation techniques for apps that run on the Android operating systems. At this point in time, there are in fact a number of such techniques in the literature, which differ in the way they generate inputs, the strategy they use to explore the behavior of the app under test, and the specific heuristics they use. To better understand the strengths and weaknesses of these existing approaches, and get general insight on ways they could be made more effective, in this paper we perform a thorough comparison of the main existing test input generation tools for Android. In our comparison, we evaluate the effectiveness of these tools, and their corresponding techniques, according to four metrics: ease of use, ability to work on multiple platforms, code coverage, and ability to detect faults. Our results provide a clear picture of the state of the art in input generation for Android apps and identify future research directions that, if suitably investigated, could lead to more effective and efficient testing tools for Android.

Subfields: Information Systems, Software, Signal Processing

View Paper: https://www.semanticscholar.org/paper/c2f1c8abdf8b42cbe94e3cf3ddd114cf8ef658c9

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Citations in the corpus

*droid (2016, background): “Indeed, prior efforts have focused on developing a taxonomy of Android security research [Sufatrio et al. 2015], examining the research devoted to appiﬁcation challenges [Acar et al. 2016], and investigating the state of the art of input generation for dynamic testing [Choudhary et al. 2015].”
Guided, stochastic model-based GUI testing of Android apps (2017, background): “They have the best performance in their own approach categories [5].”
Guided GUI testing of android apps with minimal restart and approximate learning (2013, methodology): “’s survey [24] suggests that Monkey does achieve good test coverage by injecting user actions quickly, despite this limitation.”
DL-Droid: Deep learning based android malware detection using real devices (2019, unknown): “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.…”
DroidBot: A Lightweight UI-Guided Test Input Generator for Android (2017, unknown): “According to a recent survey [2], most approaches make use of either app instrumentation or system modification in order to get enough information to guide testing.”
Robustness Testing of Autonomy Software (2017, methodology): “such as combinatorial testing [33] or the testing ecosystem that has sprung up around Android testing [7], it’s possible that these techniques could be used in the robotic systems domain by making similar modifications to them as those presented in this paper.”