Annotated buzzwords and key references for software testing in the cloud

Bertolino, Antonia; Nautiyal, Lata; Preeti, .

doi:10.1109/ccaa.2017.8229933

Cited by 5 publications

(16 citation statements)

References 28 publications

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“…To say the truth, CC is so named because a virtual cloud is often used to hide unwanted knowledge of inner workings; the end-users are none the wiser (Abdu et al, 2017). Moreover, the real physical and virtual digital clouds have a correlated meaning with a high beautiful shelter that covers everything related to daily life (Bertolino, Nautiyal and Malik, 2017).…”

Section: Cloud Computingmentioning

confidence: 99%

“…Many researchers, practitioners, analysts, and designers have proposed definitions and explanations for CC. But, until now, CC has no formally accepted conceptualization which adequately addresses its reality; though it has not yet been sufficient standardized (Chowdhary and Rawat, 2013) (Alessio et al, 2014) (Bertolino, Nautiyal and Malik, 2017). As a matter of fact, it is a rapidly evolving technology that overlaps with the other existing technologies (George Pallis, 2010).…”

Section: Characteristicmentioning

confidence: 99%

“…But in its simplest form, CC can be portrayed within this context of content outsourcing, data, and processes as virtual servers available over the Internet (Yashodha Sambrani, 2016) (Ali, 2016). The company that can provide and complement these activities as measured services is referred to as a cloud vendor (Bertolino, Nautiyal and Malik, 2017).…”

Section: Characteristicmentioning

confidence: 99%

“…However, it is the most expensive model among the other models. Regardless of their architecture, the same data-center can provide a mechanism to share different well-suited infrastructure resources among various end-users (Ramzan and Alawairdhi, 2014) (Bertolino, Nautiyal and Malik, 2017). These hosted resource elements are interconnected and appeared to the clients as a single coherent entity according to their needs (Ramzan and Alawairdhi, 2014).…”

Section: Infrastructure-as-a-service (Iaas)mentioning

confidence: 99%

“…These hosted resource elements are interconnected and appeared to the clients as a single coherent entity according to their needs (Ramzan and Alawairdhi, 2014). This allows CSPs to efficiently target a variety of customers' groups according to their needs and preferences (Bertolino, Nautiyal and Malik, 2017). All the offered resource elements of this model are in related to the base cloud infrastructure and may include, but are not limited to, OSs, servers, service computational power, storage-related services, memory, networking capabilities, bandwidth, input/output facilities, and other essential computational resource elements that meet different customers' needs (Gumbi and Mnkandla, 2015) (Ali, 2016).…”

Section: Infrastructure-as-a-service (Iaas)mentioning

confidence: 99%

See 4 more Smart Citations

Cloud IoT as a Crucial Enabler: a Survey and Taxonomy

El-Omari¹

2019

MAS

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Commonly, the technology of information scales by an order of magnitude and with high probability reinvents itself every five years or so. However, the long-standing dream has definitely become a reality today that saying you need merely a credit card to get on-demand instant accesses to a large pool of thousands, if not millions of computers founded in tens of data centers scattered across the globe. As a matter of fact, Cloud Computing is indeed a new radical paradigm shift that evolved out of utmost needs for hosting and delivering all the things electronically as well-defined services over the Internet. Its aim is not only to provide improved computerized services but also innovative ones to every user from ordinary-home end-users to professional workers. Another further long-held dream of computing that has recently emerged as a reality is the CloudIoT paradigm where the cloud-based application platforms are enhanced to generate smart decisions and usable intelligence based on Internet-connected semi-autonomous smart small sensors that can sense, interrupt, and interchange data between each other as well as with the same computing clouds. With the intention of reaching the right cloud computing vision, it is, however, insufficient to just track a set of research and development actions that address the major concerns without practical support from both industrial and research communities. Rather, there is a necessity to enact a series of insight development strategies and policies that not only ensure that the right issues are well-timed addressed, but also that the most appropriate actions are taken and accomplished. Additionally, one of the key points to consider in this paper is that this in-depth evaluation of using Cloud computing may find points lacking in the cloud environments that could open up new research opportunities to be further investigated or enable new speed-to-market scenarios.

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Section: Cloud Computingmentioning

confidence: 99%

Section: Characteristicmentioning

confidence: 99%

Section: Characteristicmentioning

confidence: 99%

Section: Infrastructure-as-a-service (Iaas)mentioning

confidence: 99%

Section: Infrastructure-as-a-service (Iaas)mentioning

confidence: 99%

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Cloud IoT as a Crucial Enabler: a Survey and Taxonomy

El-Omari¹

2019

MAS

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Testing as a service (TaaS)

Girardon

Costa

Machado

et al. 2020

Proceedings of the 35th Annual ACM Symposium on Applied Computing

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Fuzzy Multicriteria Decision-Making Approach for Measuring the Possibility of Cloud Adoption for Software Testing

Ali

Ullah²,

Abrar

et al. 2020

Scientific Programming

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To reduce costs and improve organizational efficiency, the adoption of innovative services such as Cloud services is the current trend in today’s highly competitive global business venture. The aim of the study is to guide the software development organization (SDO) for Cloud-based testing (CBT) adoption. To achieve the aim, this study first explores the determinants and predictors of Cloud adoption for software testing. Grounded on the collected data, this study designs a technology acceptance model using fuzzy multicriteria decision-making (FMCDM) approach. For the stated model development, this study identifies a list of predictors (main criteria) and factors (subcriteria) using systematic literature review (SLR). In the results of SLR, this study identifies seventy subcriteria also known as influential factors (IFs) from a sample of 136 papers. To provide a concise understanding of the facts, this study classifies the identified factors into ten predictors. To verify the SLR results and to rank the factors and predictors, an empirical survey was conducted with ninety-five experts from twenty different countries. The application value in the industrial field and academic achievement of the present study is the development of a general framework incorporating fuzzy set theory for improving MCDM models. The model can be applied to predict organizational Cloud adoption possibility taking various IFs and predictors as assessment criteria. The developed model can be divided into two main parts, ranking and rating. To measure the success or failure contribution of the individual IFs towards successful CBT adoption, the ranking part of the model will be used, while for a complete organizational assessment in order to identify the weak area for possible improvements, the assessment part of the model will be used. Collectively, it can be used as a decision support system to gauge SDO readiness towards successful CBT.

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Annotated buzzwords and key references for software testing in the cloud

Cited by 5 publications

References 28 publications

Cloud IoT as a Crucial Enabler: a Survey and Taxonomy

Cloud IoT as a Crucial Enabler: a Survey and Taxonomy

Testing as a service (TaaS)

Fuzzy Multicriteria Decision-Making Approach for Measuring the Possibility of Cloud Adoption for Software Testing

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