Fuzzy Multicriteria Decision-Making Approach for Measuring the Possibility of Cloud Adoption for Software Testing
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) ado...
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Series: | Scientific Programming |
Online Access: | http://dx.doi.org/10.1155/2020/6597316 |
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doaj-5f26702f84cd46c38a569d730e6019862021-07-02T10:09:10ZengHindawi LimitedScientific Programming1058-92441875-919X2020-01-01202010.1155/2020/65973166597316Fuzzy Multicriteria Decision-Making Approach for Measuring the Possibility of Cloud Adoption for Software TestingSikandar Ali0Niamat Ullah1Muhammad Faisal Abrar2Zhongguo Yang3Jiwei Huang4Department of Computer Science and Technology, China University of Petroleum, Beijing 102249, ChinaDepartment of Computer Science University of Buner, Buner 17290, PakistanDepartment of Computer Software Engineering, University of Engineering and Technology, Mardan 23200, PakistanSchool of Information Science and Technology, North China University of Technology, Beijing, ChinaDepartment of Computer Science and Technology, China University of Petroleum, Beijing 102249, ChinaTo 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.http://dx.doi.org/10.1155/2020/6597316 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Sikandar Ali Niamat Ullah Muhammad Faisal Abrar Zhongguo Yang Jiwei Huang |
spellingShingle |
Sikandar Ali Niamat Ullah Muhammad Faisal Abrar Zhongguo Yang Jiwei Huang Fuzzy Multicriteria Decision-Making Approach for Measuring the Possibility of Cloud Adoption for Software Testing Scientific Programming |
author_facet |
Sikandar Ali Niamat Ullah Muhammad Faisal Abrar Zhongguo Yang Jiwei Huang |
author_sort |
Sikandar Ali |
title |
Fuzzy Multicriteria Decision-Making Approach for Measuring the Possibility of Cloud Adoption for Software Testing |
title_short |
Fuzzy Multicriteria Decision-Making Approach for Measuring the Possibility of Cloud Adoption for Software Testing |
title_full |
Fuzzy Multicriteria Decision-Making Approach for Measuring the Possibility of Cloud Adoption for Software Testing |
title_fullStr |
Fuzzy Multicriteria Decision-Making Approach for Measuring the Possibility of Cloud Adoption for Software Testing |
title_full_unstemmed |
Fuzzy Multicriteria Decision-Making Approach for Measuring the Possibility of Cloud Adoption for Software Testing |
title_sort |
fuzzy multicriteria decision-making approach for measuring the possibility of cloud adoption for software testing |
publisher |
Hindawi Limited |
series |
Scientific Programming |
issn |
1058-9244 1875-919X |
publishDate |
2020-01-01 |
description |
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. |
url |
http://dx.doi.org/10.1155/2020/6597316 |
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