Testing android apps through symbolic execution

Mirzaei, Nariman; Malek, Sam; Păsăreanu, Corina S.; Esfahani, Naeem; Mahmood, Riyadh

doi:10.1145/2382756.2382798

Cited by 115 publications

(66 citation statements)

References 18 publications

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“…Several recent efforts [10,17,23] have applied symbolic execution [12,15,18] to generate inputs to Android apps. Symbolic execution automatically partitions the domain of inputs such that each partition corresponds to a unique program behavior (e.g., execution of a unique program path).…”

Section: Related Workmentioning

confidence: 99%

Dynodroid: an input generation system for Android apps

Machiry

Tahiliani

Naik

2013

Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering

583

379

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We present a system Dynodroid for generating relevant inputs to unmodified Android apps. Dynodroid views an app as an event-driven program that interacts with its environment by means of a sequence of events through the Android framework. By instrumenting the framework once and for all, Dynodroid monitors the reaction of an app upon each event in a lightweight manner, using it to guide the generation of the next event to the app. Dynodroid also allows interleaving events from machines, which are better at generating a large number of simple inputs, with events from humans, who are better at providing intelligent inputs.We evaluated Dynodroid on 50 open-source Android apps, and compared it with two prevalent approaches: users manually exercising apps, and Monkey, a popular fuzzing tool. Dynodroid, humans, and Monkey covered 55%, 60%, and 53%, respectively, of each app's Java source code on average. Monkey took 20X more events on average than Dynodroid. Dynodroid also found 9 bugs in 7 of the 50 apps, and 6 bugs in 5 of the top 1,000 free apps on Google Play.

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Section: Related Workmentioning

confidence: 99%

Dynodroid: an input generation system for Android apps

Machiry

Tahiliani

Naik

2013

Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering

583

379

View full text Add to dashboard Cite

show abstract

“…There are studies that apply symbolic execution to mobile application debugging [5], [10]. However, mobile apps may have different behaviors depending on the device on which they run, and it would be hard to reproduce those differences among devices through static execution; hence, these approaches are not suitable for examining the unintended behaviors on specific devices that our work focuses on.…”

Section: Related Workmentioning

confidence: 99%

LogChamber: Inferring Source Code Locations Corresponding to Mobile Applications Run-time Logs

Ono

Sakurai

Yamane

2016

Journal of Information Processing

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We present a development support tool, called LogChamber, which infers source-code locations by analyzing run-time logs of mobile applications. During development, developers insert log functions into applications calls in order to confirm that the applications correctly run as expected. After that, they need to have a process for estimating a program's runtime behavior in order to identify the locations of unintended behavior. Such processes rely on the abilities of the developers and are not easy in many cases. Most runtime environments of mobile applications provide only limited resources, and as a result, cannot save sufficiently many runtime logs. The situation is made even worse by careless insertions of log-function calls. The method presented in this paper analyzes static source code and runtime logs. After that, it supports developers by quickly inferring candidates of log function calls. For fast inference of candidates, it extracts log strings from the source code and constructs an index of their locations in advance. We implemented our method as a plugin tool on Android Studio, one of the major integrated development environments for Android applications. We report our experiments with the implementation on real open-source applications.

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“…Gross et al developed EXSYST [20] that uses search based testing to improve GUI testing coverage. Mirzaei et al applied symbolic execution to generate test cases for Android apps [27]. AsDroid could potentially leverage these techniques to generate test cases for bug report validation.…”

Section: Related Workmentioning

confidence: 99%

AsDroid: detecting stealthy behaviors in Android applications by user interface and program behavior contradiction

Huang

Zhang

Tan

et al. 2014

Proceedings of the 36th International Conference on Software Engineering

179

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Android smartphones are becoming increasingly popular. The open nature of Android allows users to install miscellaneous applications, including the malicious ones, from third-party marketplaces without rigorous sanity checks. A large portion of existing malwares perform stealthy operations such as sending short messages, making phone calls and HTTP connections, and installing additional malicious components. In this paper, we propose a novel technique to detect such stealthy behavior. We model stealthy behavior as the program behavior that mismatches with user interface, which denotes the user's expectation of program behavior. We use static program analysis to attribute a top level function that is usually a user interaction function with the behavior it performs. Then we analyze the text extracted from the user interface component associated with the top level function. Semantic mismatch of the two indicates stealthy behavior. To evaluate AsDroid, we download a pool of 182 apps that are potentially problematic by looking at their permissions. Among the 182 apps, AsDroid reports stealthy behaviors in 113 apps, with 28 false positives and 11 false negatives.

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Testing android apps through symbolic execution

Cited by 115 publications

References 18 publications

Dynodroid: an input generation system for Android apps

Dynodroid: an input generation system for Android apps

LogChamber: Inferring Source Code Locations Corresponding to Mobile Applications Run-time Logs

AsDroid: detecting stealthy behaviors in Android applications by user interface and program behavior contradiction

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