Dynodroid: an input generation system for Android apps

By Aravind Kumar Machiry, Rohan Tahiliani, Mayur Naik. ESEC/FSE 2013, 2013

745 citations recorded by the source corpus.

Subfields: Information Systems, Software, Signal Processing

View Paper: https://www.semanticscholar.org/paper/0b7f62a2ac217e035e0cd9cb73d2de4fb6135af5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

1. Koushik Sen
2. Zhendong Su
3. George C. Necula
4. Wontae Choi
5. Yue Jia
6. Mark Harman
7. Ke Mao
8. Lorenzo Cavallaro

In Software

1. Koushik Sen
2. Yue Jia
3. George C. Necula
4. Wontae Choi
5. Mark Harman
6. Zhendong Su
7. Ke Mao
8. Yuting Chen

In Signal Processing

1. Koushik Sen
2. George C. Necula
3. Wontae Choi
4. Mark Harman
5. Yue Jia
6. Ke Mao
7. Zhendong Su
8. Lorenzo Cavallaro

Citations in the corpus

• PUMA: programmable UI-automation for large-scale dynamic analysis of mobile apps (2014, methodology): “These analyses have been used for analyzing performance [26, 27, 15], bugs [22, 25, 19], privacy and security [11, 24], compliance [20] and correctness [18], of apps, some at a scale of thousands of apps.”
• Sapienz: multi-objective automated testing for Android applications (2016, methodology): “Therefore, we follow the practice adopted by some previous authors [37, 52] of applying the technique to a large number of popular apps in Google Play.”
• DroidBot: A Lightweight UI-Guided Test Input Generator for Android (2017, methodology): “DynoDroid [8] also generates randomized input, but it is smarter in selecting test inputs.”
• 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].”
• *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].”
• Vetting undesirable behaviors in android apps with permission use analysis (2013, methodology): “The Application Driver could be enhanced with an automatic input generation system such as AppsPlayground [48], AppInspector [34], Dynodroid [43], or guided analysis technique such as multi-path exploration [44], forced/informed execution [54,55].”
• AppScanner: Automatic Fingerprinting of Smartphone Apps from Encrypted Network Traffic (2016, methodology): “Greater coverage of all the network flows in an app may theoretically be obtained by using advanced UI fuzzing techniques provided by frameworks such as Dynodroid [19], or by recruiting human participants.”
• Guided GUI testing of android apps with minimal restart and approximate learning (2013, methodology): “MacHiry et al.[25] suggest a technique to infer representative set of events and perform event aware random testing.”
• EvoDroid: segmented evolutionary testing of Android apps (2014, methodology): “Therefore, Monkey’s test coverage is shallow as others have confirmed in [32].”
• 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).”
• ReCon: Revealing and Controlling PII Leaks in Mobile Network Traffic (2015, background): “Tools such as Dynodroid [47], PUMA [30], and A3E [14] automatically generate UI events to explore UI states, but require manual input for more complex user interactions, e.g., logging in to sites [20].”
• 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.”
• Automated Test Input Generation for Android: Are We There Yet? (E) (2015, positive): “Dynodroid [18] is also based on random exploration, but it has several features that make its exploration more efficient compared to Monkey.”
• A Survey of Human-in-the-loop for Machine Learning (2021, background): “[119] designed a test generation system called Dynodroid for fuzzing unmodified Android applications.”
• DL-Droid: Deep learning based android malware detection using real devices (2019, methodology): “…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. (2013)…”
• The Evolution of Android Malware and Android Analysis Techniques (2017, methodology): “As Android apps are highly interactive, many behaviors need to be triggered via the interface, received intents, or with smart, automatic event injectors [Azim and Neamtiu 2013; Machiry et al. 2013; Mahmood et al. 2014].”