Building a Cloud-Based Mobile Application Testbed

Turner, Hamilton; White, Jules; Reed, Jeff; Galindo, José; Porter, Adam; Marathe, Madhav V.; Vullikanti, Anil; Gokhale, Aniruddha

doi:10.4018/978-1-4666-2919-6.ch040

Cited by 3 publications

(2 citation statements)

References 52 publications

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“…The privacy of mobile phone users has been the focus of an ongoing discussion within the security community [50,51]. Researchers have modeled the variability existing in the Android emulator to optimize the selection of the tests to execute when developing Android apps [52,53]. Furthermore, they have modeled the different traceability relationships in between the Android application structure, the emulator options, and the permissions system in Android.…”

Section: Rq3: Applying Invar In Realistic Configuration Settings -Andmentioning

confidence: 99%

Supporting distributed product configuration by integrating heterogeneous variability modeling approaches

Galindo

Dhungana

Rabiser

et al. 2015

Information and Software Technology

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Context: In industrial settings products are developed by more than one organization. Software vendors and suppliers commonly typically maintain their own product lines, which contribute to a larger (multi) product line or software ecosystem. It is unrealistic to assume that the participating organizations will agree on using a specific variability modeling technique-they will rather use different approaches and tools to manage the variability of their systems. Objective: We aim to support product configuration in software ecosystems based on several variability models with different semantics that have been created using different notations. Method: We present an integrative approach that provides a unified perspective to users configuring products in multi product line environments, regardless of the different modeling methods and tools used internally. We also present a technical infrastructure and a prototype implementation based on web services. Results: We show the feasibility of the approach and its implementation by using it with the three most widespread types of variability modeling approaches in the product line community, i.e., feature-based, OVM-style, and decision-oriented modeling. To demonstrate the feasibility and flexibility of our approach, we present an example derived from industrial experience in enterprise resource planning. We further applied the approach to support the configuration of privacy settings in the Android ecosystem based on multiple variability models. We also evaluated the performance of different model enactment strategies used in our approach. Conclusions: Tools and techniques allowing stakeholders to handle variability in a uniform manner can considerably foster the initiation and growth of software ecosystems from the perspective of software reuse and configuration.

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Section: Rq3: Applying Invar In Realistic Configuration Settings -Andmentioning

confidence: 99%

Supporting distributed product configuration by integrating heterogeneous variability modeling approaches

Galindo

Dhungana

Rabiser

et al. 2015

Information and Software Technology

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“…A white-box automated security testing approach [34] for cloud-based Android apps is evaluated by an example run over 1,000 test cases using 100 parallel instances. The study [35] presents a testing approach with experiments evaluated on a combination of 1,000+ emulated instances and 10 actual devices.…”

Section: ) Mobile Testingmentioning

confidence: 99%

A Systematic Mapping Study of Empirical Studies on Software Cloud Testing Methods

Ahmad

Brereton

András

2017

2017 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)

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Abstract-Context: Software has become more complicated, dynamic, and asynchronous than ever, making testing more challenging. With the increasing interest in the development of cloud computing, and increasing demand for cloud-based services, it has become essential to systematically review the research in the area of software testing in the context of cloud environments. Objective: The purpose of this systematic mapping study is to provide an overview of the empirical research in the area of software cloud-based testing, in order to build a classification scheme. We investigate functional and non-functional testing methods, the application of these methods, and the purpose of testing using these methods. Method: We searched for electronically available papers in order to find relevant literature and to extract and analyze data about the methods used. Result: We identified 69 primary studies reported in 75 research papers published in academic journals, conferences, and edited books. Conclusion: We found that only a minority of the studies combine rigorous statistical analysis with quantitative results. The majority of the considered studies present early results, using a single experiment to evaluate their proposed solution.

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