A context simulator as testing support for mobile apps

Vieira, Vaninha; Holl, Konstantin; Hassel, Michael

doi:10.1145/2695664.2695782

Cited by 14 publications

(16 citation statements)

References 14 publications

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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%

“…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%

“…Vieira et al [37] argue that testing context-aware applications in the lab is difficult because of the number of different scenarios and situations that a user might The modeling in the context simulator is made in four different levels:…”

Section: Context Simulatormentioning

confidence: 99%

“…The context simulator supports a large variety of context sources, 22 contexts divided into 6 categories supporting 41 context sources [37].…”

Section: Context Simulatormentioning

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: Rq 11: What Technique Do They Implement?mentioning

confidence: 99%

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

confidence: 99%

Section: Context Simulatormentioning

confidence: 99%

“…The context simulator supports a large variety of context sources, 22 contexts divided into 6 categories supporting 41 context sources [37].…”

Section: Context Simulatormentioning

confidence: 99%

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

2019

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“…Vieira et al [Vieira et al, 2015] focus their work on applications that are required to work under certain conditions, such as one that needs to be at a certain country or that aims at panic situations in major events. Thus, they propose a Context Simulator that has two main components: a desktop one and a mobile one.…”

Section: Maintainabilitymentioning

confidence: 99%

Automated Pattern-Based Testing of Mobile Applications

Morgado

Paiva

Faria

2014

2014 9th International Conference on the Quality of Information and Communications Technology

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Testing is essential to improve the quality of a product and is, thus, a crucial part of any development process. However, the amount of time and resources companies dedicate to it is usually reduced, which justifies the high focus of the community on improving testing automation. Considering the importance smartphones have acquired in our daily lives, it is extremely important to define techniques to test mobile applications in order to ensure their quality.This document presents a testing approach and tool (iMPAcT) to improve the automation of mobile testing based on the presence of recurring behaviour, UI Patterns. It combines reverse engineering, pattern matching and testing. The reverse engineering process is responsible for crawling the application, i.e. to analyse the state of the application and to interact with it by firing events. The pattern matching tries to identify the presence of UI patterns based on a catalogue of patterns. When a UI Pattern from the catalogue is detected, a test strategy is applied. These test strategies are called UI Test Patterns. These three phases work in an iterative way: the patterns and identified and tested between firing of events, i.e. the process alternates between exploring the application and testing the UI Patterns. The process is dynamic and fully automatic not requiring any previous knowledge about the application under test.A catalogue of patterns is presented in this document. It has UI Patterns to identify on mobile applications and UI Test Patterns defining generic test strategies to test them. These patterns are generic and, thus, can be applied to any application.In order to validate the overall approach we have conducted three experiments. The goal is to analyse the capacity of iMPAcT of finding failures and assess if the results are reliable. In addition, we also measured the execution time and events coverage. Este documento apresenta uma abordagem de teste, e respetiva ferramenta (iMPAcT), para melhorar a automação de teste de aplicações móveis baseado na presença de comportamento recorrente, padrões de UI. Esta abordagem combina engenharia reversa, deteção de padrões e teste. O processo de engenharia reversa é responsável pela exploração da aplicação, ou seja, pela análise do estado da aplicação e a interação com ela através de eventos. O processo de deteção de padrões baseia-se num catálogo de padrões para identificar padrões de UI. Quando um padrão de UI do catálogo é detetado, uma estratégia de teste é aplicada. Estas estratégias de teste denominam-se padrões de teste. Estas três fases trabalham de forma iterativa: os padrões são identificados e testados entre eventos, ou seja, o processo alterna entre a exploração da aplicação e o teste dos padrões de UI. O processo é dinâmico e completamente automático e não necessita de qualquer conhecimento prévio acerca da aplicação a testar.Um catálogo de padrões é apresentado neste documento. Contém padrões de UI para serem identificados em aplicações móveis e padrões de teste que definem estratégias ...

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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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A context simulator as testing support for mobile apps

Cited by 14 publications

References 14 publications

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

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

Automated Pattern-Based Testing of Mobile Applications

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

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