A model-based approach to test automation for context-aware mobile applications

Griebe, Tobias; Gruhn, Volker

doi:10.1145/2554850.2554942

Cited by 31 publications

(29 citation statements)

References 17 publications

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“…By studying the papers of Griebe and Gruhn [44], Vieira et al [37], and Amalfitano et al [42], we found two papers related to testing context-aware Android applications: Mirza and Khan [110] and Luo et al [111]. The corresponding tools are presented in the sequel.…”

Section: Other Tools From Cited Papersmentioning

confidence: 99%

“…Thus, some tools test Android applications not looking at the components of the interface, but rather through events generated for the application simulating the user touching the screen, events from the system, and also through data from the sensors of the device. For these tools, we [90], CrashScope [18,52], DroidBot [19], M[agi]C [58], ACTEve [47], PATS [77], BBOXTESTER [92], AppDoctor [63], ORBIT [39], Fest [45], Easy-Mock [45], Hamcrest [45], JUnit [45], Robolectric [45], Robotium [45] [79], Android.Test [45], DroidCrawler [54], Custom-built version of the calabashandroid [44], CATE [57], MobiGUITAR [105], [41], Context Simulator [37], ADAutomation [38], MAT [46], AM-TaaS [49], VTE [55], [59], ACRT [56], EHB-Droid [64], ATG [73], SPAG-C [76], Appetizer [79], Bot-bot [79], Culebra [79], monkeyrunner [79], Mosaic [79], Ranorex [79], HiroMacro [79], Repeti-Touch [79], MAFT [82], MBTS4MA [83], SIG-Droid …”

Section: Rq 11: What Technique Do They Implement?mentioning

confidence: 99%

“…From the 68 selected studies, Griebe and Gruhn [44], Vieira et al [37], and Amalfitano et al [42] explicitly focus on testing of context-aware applications. The "Custom-built version of the Calabash-Android" section, "Context simulator" section.…”

Section: Rq 2: What Are the Android Context-aware Testing Studies Andmentioning

confidence: 99%

“…Griebe and Gruhn [44] propose a model-based approach to improve the testing of context-aware mobile applications. Their approach is based on a four-tier process system as follows:…”

Section: Custom-built Version Of the Calabash-androidmentioning

confidence: 99%

“…In order to answer this research question, we have observed the publications of the authors of the Android context-aware testing studies, such as Griebe and Gruhn [44], Vieira et al [37], and Amalfitano et al [42].…”

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

confidence: 99%

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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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Section: Other Tools From Cited Papersmentioning

confidence: 99%

Section: Rq 11: What Technique Do They Implement?mentioning

confidence: 99%

Section: Rq 2: What Are the Android Context-aware Testing Studies Andmentioning

confidence: 99%

“…Griebe and Gruhn [44] propose a model-based approach to improve the testing of context-aware mobile applications. Their approach is based on a four-tier process system as follows:…”

Section: Custom-built Version Of the Calabash-androidmentioning

confidence: 99%

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

confidence: 99%

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

2019

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Testing of adaptive and context‐aware systems: approaches and challenges

Siqueira

Ferrari

Souza

et al. 2021

Software Testing Verif & Rel

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Adaptive systems (ASs) and context-aware systems (CASs) are able to evaluate their own behaviour and to adapt it when the system fails to accomplish its goals or when better functionality or performance is possible. Ensuring the reliability of ASs and CASs is demanding because failures might have undesirable consequences. Testing ASs and CASs effectively is not trivial because of the inherent characteristics of these systems. The literature lacks a comprehensive review that provides a broad picture of the area; current reviews are outdated and incomplete. The objectives of this study are characterizing the state of the art in AS and CAS testing and discussing approaches, challenges, observed trends, and research limitations and directions. We performed a systematic literature review (SLR) and a thematic analysis of studies, reporting up-to-date, refined and extended results when compared with existing reviews. Based on 102 selected studies, we (i) characterized testing approaches by grouping techniques for ASs and CASs; (ii) updated and refined a characterization of testing challenges for ASs and CASs; and (iii) analysed and discussed research trends and implications for AS and CAS testing. There are recurring research concerns regarding AS and CAS testing. Examples are the generation of test cases and built-in tests. Moreover, we also identified recurring testing challenges such as context monitoring and runtime decisions. Moreover, there are some trends such as model-based testing and hybrid techniques and some little investigated issues like uncertainty and prediction of changes. All in all, our results may provide guidance for developers and researchers with respect to the practice and the future research on AS and CAS testing.

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Mobile Applications Testing Based on Bigraphs and Dynamic Feature Petri Nets

Nguyễn

Aktouf

et al. 2022

Intelligence of Things: Technologies and Applications

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A model-based approach to test automation for context-aware mobile applications

Cited by 31 publications

References 17 publications

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

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

Testing of adaptive and context‐aware systems: approaches and challenges

Mobile Applications Testing Based on Bigraphs and Dynamic Feature Petri Nets

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