Capture-Replay Testing for Android Applications

Liu, Chien-Hung; Lu, Chien Yu; Cheng, Shan; Chang, Koan Yuh; Hsiao, Yung Chia; Chu, Weng Ming

doi:10.1109/is3c.2014.293

Cited by 31 publications

(4 citation statements)

References 7 publications

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“…It was found that random exploration strategies by Monkey 5 or Dynodroid [29] were more effective than more sophisticated approaches. Although our work does not scope AIG tools, very often they use UI automation frameworks (e.g., UIAutomator and Robotium) underneath their systems [30], [31], [32], [33]. Results and insights about energy consumption in our study may also apply to tools that build on top of UI automation frameworks.…”

Section: Related Workmentioning

confidence: 91%

On the Energy Footprint of Mobile Testing Frameworks

Cruz

Abreu

2021

IIEEE Trans. Software Eng.

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High energy consumption is a challenging issue that an ever increasing number of mobile applications face today. However, energy consumption is being tested in an ad hoc way, despite being an important non-functional requirement of an application. Such limitation becomes particularly disconcerting during software testing: on the one hand, developers do not really know how to measure energy; on the other hand, there is no knowledge as to what is the energy overhead imposed by the testing framework. In this paper, as we evaluate eight popular mobile UI automation frameworks, we have discovered that there are automation frameworks that increase energy consumption up to roughly 2200%. While limited in the interactions one can do, Espresso is the most energy efficient framework. However, depending on the needs of the tester, Appium, Monkeyrunner, or UIAutomator are good alternatives. In practice, results show that deciding which is the most suitable framework is vital. We provide a decision tree to help developers make an educated decision on which framework suits best their testing needs.

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Section: Related Workmentioning

confidence: 91%

On the Energy Footprint of Mobile Testing Frameworks

Cruz

Abreu

2021

IIEEE Trans. Software Eng.

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“…It mainly focuses on the unit testing and automated acceptance testing with Android devices. Acceptance testing supports different Android automation tools, including Monkey [10], MonkeyTalk [11], Robotium [12], UiAutomator [13] and Robot Framework [14]. In the STF-CTP environment, test jobs will be dispatched by Rabbit Message Queue [15].…”

Section: Stf-ctpmentioning

confidence: 99%

A Study of Resource Utilization Improvement on Cloud Testing Platform

2021

KSII TIIS

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This paper developed the software testing factory-cloud testing platform (STF-CTP) to address the software compatible issues in various smart devices. Software developers who only require uploading the application under test (AUT) and test script can test plenty of smart devices in STF-CTP. The challenge for the cloud test platform is how to optimize the resource and increase the performance in the limited resource. This paper proposed a new scheduling mechanism and a new process of the system operation which is based on the OpenStack platform. We decrease about 40% memory usage of OpenStack server, increase 3% to 10% Android device usage of STF-CTP, enhance about 80% test job throughput and reduces about 40% test job average waiting time.

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“…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%

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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Capture-Replay Testing for Android Applications

Cited by 31 publications

References 7 publications

On the Energy Footprint of Mobile Testing Frameworks

On the Energy Footprint of Mobile Testing Frameworks

A Study of Resource Utilization Improvement on Cloud Testing Platform

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

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