Reducing combinatorics in GUI testing of android applications

Mirzaei, Nariman; Garcia, Joshua; Bagheri, Hamid; Sadeghi, Alireza; Malek, Sam

doi:10.1145/2884781.2884853

Cited by 123 publications

(58 citation statements)

References 30 publications

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“…The other relevant line of research focuses on applications of recovered architecture in a variety of mobile software engineering problems ( Bagheri et al, 2015;2016;Mirzaei et al, 2016;Schmerl et al, 2015 ). Among others, COVERT ( Bagheri et al, 2015 ) showed the power of software architectural abstractions for the analysis of security properties in Android apps.…”

Section: Related Workmentioning

confidence: 99%

“…Joorabchi and Mesbah (2012) presented a reverse engineering technique to automatically navigate a given iPhone app, and to recover its architectural model at the GUI level. Mirzaei et al (2016) used the recovered architecture of apps, mainly at the GUI-level, for combinatorial, yet scalable, GUI testing of Android apps. TrimDroid leveraged the dependencies among the architectural elements comprising a given app to reduce the number of combinations in generated test cases.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Software architectural principles in contemporary mobile software: from conception to practice

Bagheri

Garcia

Sadeghi

et al. 2016

Journal of Systems and Software

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a b s t r a c tThe meteoric rise of mobile software that we have witnessed in the past decade parallels a paradigm shift in its design, construction, and deployment. In particular, we argue that today's mobile software, with its rich ecosystem of apps, would have not been possible without the pioneering advances in software architecture research in the decade that preceded it. We describe the drivers that elevated software architecture to the centerpiece of contemporary mobile software. We distill the architectural principles found in Android, the predominant mobile platform with the largest market share, and trace those principles to their conception at the turn of century in software architecture literature. Finally, to better understand the extent to which Android's ecosystem of apps employs architectural concepts, we mine the reverse-engineered architecture of hundreds of Android apps in several app markets and report on those results.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Software architectural principles in contemporary mobile software: from conception to practice

Bagheri

Garcia

Sadeghi

et al. 2016

Journal of Systems and Software

Self Cite

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“…Automated GUI Testing Many GUI testing techniques, e.g., random testing [57], search-based testing [58,60], symbolic execution [5,63,82], model-based testing [2,3,8,11,76,78,85] and other approaches [61,62,75,83], have been proposed for Android apps. As we discussed in Section 1 and demonstrated in the evaluation, these techniques are ineffective for APEs due to lack of prior knowledge (fault patterns).…”

Section: Related Workmentioning

confidence: 99%

Efficiently manifesting asynchronous programming errors in Android apps

Fan

Sus

Chen

et al. 2018

Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering

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Android, the #1 mobile app framework, enforces the single-GUIthread model, in which a single UI thread manages GUI rendering and event dispatching. Due to this model, it is vital to avoid blocking the UI thread for responsiveness. One common practice is to offload long-running tasks into async threads. To achieve this, Android provides various async programming constructs, and leaves developers themselves to obey the rules implied by the model. However, as our study reveals, more than 25% apps violate these rules and introduce hard-to-detect, fail-stop errors, which we term as aysnc programming errors (APEs). To this end, this paper introduces APEChecker, a technique to automatically and efficiently manifest APEs. The key idea is to characterize APEs as specific fault patterns, and synergistically combine static analysis and dynamic UI exploration to detect and verify such errors. Among the 40 real-world Android apps, APEChecker unveils and processes 61 APEs, of which 51 are confirmed (83.6% hit rate). Specifically, APEChecker detects 3X more APEs than the state-of-art testing tools (Monkey, Sapienz and Stoat), and reduces testing time from half an hour to a few minutes. On a specific type of APEs, APEChecker confirms 5X more errors than the data race detection tool, EventRacer, with very few false alarms. CCS CONCEPTS• Software and its engineering → Software testing and debugging;such fatal programming errors that violate the rules implied by the single-UI-thread model as async programming errors (APEs).Such bugs in Android are not easy to detect manually, due to (1) they usually reside in the code of handling interactions between UI thread and async threads, which can be rather complicated for manual analysis; (2) they can only be triggered at the right states of GUI components (e.g., activity, fragment) with complicated lifecycle [21,30]; (3) they have to be triggered at right thread scheduling, while the execution time of async threads is affected by the task and its running environment (e.g., network stability, system load).Even worse, existing bug detection techniques are ineffective for such bugs. First, most GUI testing techniques, e.g., random testing [39,57], search-based testing [58,60], and model-based testing [2,3,8,78,85], are designed for functional testing in general. They aim at enumerating all possible event sequences (GUI-level events in particular) to manifest bugs, which is unscalable and time-consuming. Additionally, they mainly aim at improving code coverage, which may not be sufficient for exhibiting APEs -require specific event sequences with appropriate lifecycle states and thread scheduling. Second, static analysis tools, e.g., Lint [35], Find-Bugs [19] and PMD [67], although scalable, only enforce simple rules (syntax or trivial control/data-flow analysis) to locate suspicious bugs. For example, Lint declares it can find "WrongThread" errors (one type of APEs) [24]. However, as our evaluation in Section 5 demonstrates, Lint incurs a number of false negatives -failing to detect those so...

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“…Tools found in the SMS Tools SlumDroid[36] and GUIAnalyzer[36], Espresso[50] [45,79], Espresso Recorder[79], UIAutomator[50], Selendroid[50], Silk Mobile[50], Sikuli GUI Automation Tool[50], Segen[85], DroidMate[61], FSMdroid[70],SwiftHand [72] [72], A 3 E [89,103], TrimDroid[80], AMOGA[88], AGRippin[40], AndroidRipper[43], Extended AndroidRipper[42], T+[65], QUANTUM 6823880, Collider[51], EvoDroid[66], VeriDroid[95], JPF-ANDROID[68], Improved JPF-ANDROID[67], Thor[87], Monkey[104], Improved Monkey[53], PUMA[78], Dynodroid[62], ATT[69], Sapienz[84], DroidFuzzer [60], VALERA [71] [74, 79, 94, 96], MobiPlay [75] [79], RERAN [81] [79], Testdroid…”

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confidence: 99%

Testing tools for Android context-aware applications: a systematic mapping

2019

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Context: Mobile devices, such as smartphones, have increased their capacity of information processing and sensors have been aggregated to their hardware. Such sensors allow capturing information from the environment in which they are introduced. As a result, mobile applications that use the environment and user information to provide services or perform context-based actions are increasingly common. This type of application is known as context-aware application. While software testing is an expensive activity in general, testing context-aware applications is an even more expensive and challenging activity. Thus, efforts are needed to automate testing for context-aware applications, particularly in the scope of Android, which is currently the most used operating system by smartphones. Objective: This paper aims to identify and discuss the state-of-the-art tools that allow the automation of testing Android context-aware applications. Method: In order to do so, we carried out a systematic mapping study (SMS) to find out the studies in the existing literature that describe or present Android testing tools. The discovered tools were then analyzed to identify their potential in testing Android context-aware applications. Result: A total of 68 works and 80 tools were obtained as a result of the SMS. From the identified tools, five are context-aware Android application testing tools, and five are general Android application testing tools, but support the test of the context-aware feature. Conclusion: Although context-aware application testing tools do exist, they do not support automatic generation or execution of test cases focusing on high-level contexts. Moreover, they do not support asynchronous context variations.

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Reducing combinatorics in GUI testing of android applications

Cited by 123 publications

References 30 publications

Software architectural principles in contemporary mobile software: from conception to practice

Software architectural principles in contemporary mobile software: from conception to practice

Efficiently manifesting asynchronous programming errors in Android apps

Testing tools for Android context-aware applications: a systematic mapping

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