Towards Automated UI-Tests for Sensor-Based Mobile Applications

Griebe, Tobias; Hesenius, Marc; Gruhn, Volker

doi:10.1007/978-3-319-22689-7_1

Cited by 12 publications

(13 citation statements)

References 11 publications

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“…It re-executes all the unit test cases every time when a piece of code is changed. As an example, Hu et al [84] have applied unit testing to [26] CRASHSCOPE [27] MAMBA [28] Pretect [29] DroidMate [93] SSDA [30] TrimDroid [8] ERVA [9] Clapp et al [10] SAPIENZ [11] RacerDroid [31] Baek et al [94] DiagDroid [32] MobiPlay [95] MOTIF [33] DRUN [34] DroidDEV [96] GAT [35] Zhang et al [36] Jabbarvand et al [37] Qian et al [38] Ermuth et al [39] Cadage [97] Zhang et al [40] Zhang et al [41] dLens [42] Sonny et al [98] Packeviius et al [43] SIG-Droid [99] Knorr et al [44] TAST [100] IntentDroid [45] Griebe et al [101] Farto et al [46] Bielik et al [47] MobiGUITAR [48] AGRippin [102] Aktouf et al [49] THOR [103] AppAudit [50] Morgado et al [104] Hassanshahi et al [51] iMPAcT [52] Deng et al [53] Espada et al…”

Section: Which Test Levels Are Addressed?mentioning

confidence: 99%

“…[43] SIG-Droid [99] Knorr et al [44] TAST [100] IntentDroid [45] Griebe et al [101] Farto et al [46] Bielik et al [47] MobiGUITAR [48] AGRippin [102] Aktouf et al [49] THOR [103] AppAudit [50] Morgado et al [104] Hassanshahi et al [51] iMPAcT [52] Deng et al [53] Espada et al [54] Zhang et al [55] QUANTUM [56] CRAXDroid [57] IntentFuzzer [58] Vikomir et al [59] Shahriar et al [60] APSET [61] DROIDRACER [62] EvoDroid [105] SPAG-C [106] Caiipa [107] UGA [108] AppACTS [63] CAFA [64] Holzmann et al [109] Guo et al [65] Griebe et al [66] PBGT [67] Chen et al [110] Banerjee et al [68] Amalfitano et al [111] Adinata et al [112] A5 [69] Suarez et al [70] Linares et al [71] Sasnauskas et al [72] AMDetector [73] RERAN [15] Yang et al …”

Section: Which Test Levels Are Addressed?mentioning

confidence: 99%

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Automated Testing of Android Apps: A Systematic Literature Review

et al. 2019

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Automated testing of Android apps is essential for app users, app developers and market maintainer communities alike. Given the widespread adoption of Android and the specificities of its development model, the literature has proposed various testing approaches for ensuring that not only functional requirements but also non-functional requirements are satisfied. In this paper, we aim at providing a clear overview of the state-of-the-art works around the topic of Android app testing, in an attempt to highlight the main trends, pinpoint the main methodologies applied and enumerate the challenges faced by the Android testing approaches as well as the directions where the community effort is still needed. To this end, we conduct a Systematic Literature Review (SLR) during which we eventually identified 103 relevant research papers published in leading conferences and journals until 2016. Our thorough examination of the relevant literature has led to several findings and highlighted the challenges that Android testing researchers should strive to address in the future. After that, we further propose a few concrete research directions where testing approaches are needed to solve recurrent issues in app updates, continuous increases of app sizes, as well as the Android ecosystem fragmentation. 1 INTRODUCTION Android smart devices have become pervasive after gaining tremendous popularity in recent years. As of July 2017, Google Play, the official app store, is distributing over 3 million Android applications (i.e., apps), covering over 30 categories ranging from entertainment and personalisation apps to education and financial apps. Such popularity among developer communities can be attributed to the accessible development environment based on familiar Java programming language as well as the availability of libraries implementing diverse functionalities [1]. The app distribution ecosystem around the official store and other alternative stores such as Anzhi and AppChina is further attractive for users to find apps and organisations to market their apps [2]. Unfortunately, the distribution ecosystem of Android is porous to poorly-tested apps [3]-[5]. Yet, as reported • ξ The corresponding author.

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Section: Which Test Levels Are Addressed?mentioning

confidence: 99%

Section: Which Test Levels Are Addressed?mentioning

confidence: 99%

Automated Testing of Android Apps: A Systematic Literature Review

et al. 2019

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“…• "Towards Automated UI-Tests for Sensor-Based Mobile Applications" [112]: presents an approach that integrates sensor information into UI acceptance testing. The approach uses a sensor simulation engine to execute test cases automatically.…”

Section: Rq 21: Which Research Groups Are Involved In Android Contexmentioning

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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“…Mobile applications use combinations of regular program inputs (e.g., via touch displays), and NUI inputs (e.g., via external sensory data). To test mobile applications, Griebe et al describe a framework in which location information [6] and accelerometer data [7] can be replaced with mocked data by developers.…”

Section: Natural User Interface Testingmentioning

confidence: 99%