Automated concolic testing of smartphone apps

By Saswat Anand, Mayur Naik, Mary Jean Harrold, Hongseok Yang. SIGSOFT FSE, 2012

423 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/8517dbf9322fccae6da05d78c3ada9f1e07124df

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Citations in the corpus

Dynodroid: an input generation system for Android apps (2013, background): “Several recent efforts [9, 17, 22] have applied symbolic execution [11, 14, 18] to generate inputs to Android apps.”
Evaluating Analysis Tools for Android Apps: Status Quo and Robustness Against Obfuscation (2016, background): “This is because the two existing prototypes for symbolic execution, SymDroid [25] and ACTEve [5, 6], are still very rough prototypes that would require significant effort to make them usable for our purposes.”
Automated Test Input Generation for Android: Are We There Yet? (E) (2015, neutral): “Some tools (PUMA and SwiftHand) are compatible only with the most recent releases of the Android Framework, while others (ACTEve and Dynodroid) are bound to a very old one.”
EvoDroid: segmented evolutionary testing of Android apps (2014, methodology): “Several recent approaches [18–20, 29, 32, 38] have aimed to improve Android testing practices.”
DL-Droid: Deep learning based android malware detection using real devices (2019, neutral): “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”
Guided, stochastic model-based GUI testing of Android apps (2017, methodology): “To tackle this challenge, many techniques [2, 4, 6, 39–41] have been proposed.”
Sapienz: multi-objective automated testing for Android applications (2016, methodology): “…to evaluate in the same way, comparing against Mon-key and Dynodroid, which Choudhary et al. found to perform best among the research prototype techniques (beating recently proposed techniques including black box based An-droidRipper [15], A 3 E [20], PUMA [37] and white-box based ACTEve [16]).”
AppIntent: analyzing sensitive data transmission in android for privacy leakage detection (2013, background): “…knowledge, there are no eﬃcient solutions to this problem, and most of the existing symbolic execution approaches for GUI apps Recently, Contest [9] reduces the symbolic execution time of smartphone apps to 5%-36% of the original running time by utilizing proﬁling results, but the cost of this…”
Static analysis of android apps: A systematic literature review (2017, methodology): “JIMPLE A5 [59], ACTEve [63], android-app-analysis-tool [78], ApkCombiner [141], AppContext [88], AppIntent [90], Apposcopy [92], AppSealer [94], AutoPPG [145], Bartel et al.”
Guided GUI testing of android apps with minimal restart and approximate learning (2013, methodology): “[7] applied concolic execution [16] to guide Android app testing.”
The Evolution of Android Malware and Android Analysis Techniques (2017, background): “…to obtain detailed behavioral proﬁles [Enck et al. 2010; Zheng et al. 2012; Wei et al. 2012a; Au et al. 2012; Yan and Yin 2012; Tam et al. 2015b; Anand et al. 2012; Wei et al. 2012b] and assess the malware threat [Felt et al. 2011; Enck et al. 2011; Felt et al. 2011; Zhou and Jiang 2012b;…”
MaMaDroid: Detecting Android Malware by Building Markov Chains of Behavioral Models (2016, background): “Other approaches include random fuzzing [38], [63] and concolic testing [3], [26].”
A Grey-Box Approach for Automated GUI-Model Generation of Mobile Applications (2013, methodology): “This information is typically not available to a purely black-box analysis and is far more expensive to extract through a dynamic white-box approach such as [9].”