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) 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.

[1]  Manoj K. Jha,et al.  A cloud computing adoption in Indian SMEs: Scale development and validation approach , 2017 .

[2]  Yu-Jie Wang,et al.  Applying FMCDM to evaluate financial performance of domestic airlines in Taiwan , 2008, Expert Syst. Appl..

[3]  Katja Karhu,et al.  Trade-off between automated and manual software testing , 2011, Int. J. Syst. Assur. Eng. Manag..

[4]  T. H. Tse,et al.  5W+1H pattern: A perspective of systematic mapping studies and a case study on cloud software testing , 2016, J. Syst. Softw..

[5]  Randy H. Katz,et al.  A view of cloud computing , 2010, CACM.

[6]  Wei-Tek Tsai,et al.  Integrated fault detection and test algebra for combinatorial testing in TaaS (Testing-as-a-Service) , 2016, Simul. Model. Pract. Theory.

[7]  Priyanka,et al.  Empirical evaluation of cloud-based testing techniques , 2012 .

[8]  Jose Ignacio Aizpurua,et al.  A Model-Based Hybrid Approach for Circuit Breaker Prognostics Encompassing Dynamic Reliability and Uncertainty , 2018, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[9]  Pei-Fang Hsu,et al.  International Journal of Information Management , 2014 .

[10]  Krishn Kumar Mishra,et al.  The Impacts of Test Automation on Software's Cost, Quality and Time to Market , 2016 .

[11]  Yi Peng,et al.  Evaluation of clustering algorithms for financial risk analysis using MCDM methods , 2014, Inf. Sci..

[12]  Gary B. Wills,et al.  A framework for critical security factors that influence the decision of cloud adoption by Saudi government agencies , 2017, Telematics Informatics.

[13]  Siffat Ullah Khan,et al.  Success Factors for Software Outsourcing Partnership Management: An Exploratory Study Using Systematic Literature Review , 2017, IEEE Access.

[14]  Gary B. Wills,et al.  An empirical study of factors influencing cloud adoption among private sector organisations , 2018, Telematics Informatics.

[15]  Sujeet Kumar Sharma,et al.  Predicting motivators of cloud computing adoption: A developing country perspective , 2016, Comput. Hum. Behav..

[16]  Rakesh D. Raut,et al.  Examining the critical success factors of cloud computing adoption in the MSMEs by using ISM model , 2017 .

[17]  Rakesh D. Raut,et al.  Understanding and predicting the determinants of cloud computing adoption: A two staged hybrid SEM - Neural networks approach , 2017, Comput. Hum. Behav..

[18]  S. Justus,et al.  Cloud Testing Tools and its Challenges: A Comparative Study , 2015 .

[19]  Hamid Mcheick,et al.  Cloud Services Testing: An Understanding , 2011, ANT/MobiWIS.

[20]  Chiranjeev Kumar,et al.  A Multi Criteria Decision Making Method for Cloud Service Selection and Ranking , 2018, Int. J. Ambient Comput. Intell..

[21]  Gin-Shuh Liang,et al.  A soft computing method of performance evaluation with MCDM based on interval-valued fuzzy numbers , 2012, Appl. Soft Comput..

[22]  Janice Singer,et al.  Studying Software Engineers: Data Collection Techniques for Software Field Studies , 2005, Empirical Software Engineering.

[23]  SrikanthHema,et al.  Test case prioritization of build acceptance tests for an enterprise cloud application , 2016 .

[24]  Sikandar Ali,et al.  Software outsourcing partnership model: An evaluation framework for vendor organizations , 2016, J. Syst. Softw..

[25]  P. Lazaridis,et al.  Software Component Selection Based on Quality Criteria Using the Analytic Network Process , 2014 .

[26]  Feng Li,et al.  Cloud computing adoption by SMEs in the north east of England: A multi-perspective framework , 2013, J. Enterp. Inf. Manag..

[27]  George Candea,et al.  Cloud9: a software testing service , 2010, OPSR.

[28]  Manoj K. Jha,et al.  Analyzing the factors influencing cloud computing adoption using three stage hybrid SEM-ANN-ISM (SEANIS) approach , 2018, Technological Forecasting and Social Change.

[29]  Myra B. Cohen,et al.  Test case prioritization of build acceptance tests for an enterprise cloud application: An industrial case study , 2016, J. Syst. Softw..

[30]  Tien-Chin Wang,et al.  Using the fuzzy multi-criteria decision making approach for measuring the possibility of successful knowledge management , 2009, Inf. Sci..

[31]  CaiYan,et al.  5W+1H pattern , 2016 .

[32]  AliSikandar,et al.  Software outsourcing partnership model , 2016 .

[33]  Shah Nazir,et al.  Fuzzy logic based decision support system for component security evaluation , 2018, Int. Arab J. Inf. Technol..

[34]  Michael Diaz,et al.  How Software Process Improvement Helped Motorola , 1997, IEEE Softw..

[35]  Madeline Weiss.  APC Forum: Insights From The Top - Interviews With Two Prominent CIOs , 2013, MIS Q. Executive.

[36]  Gang Kou,et al.  Multi-attribute decision making with generalized fuzzy numbers , 2015, J. Oper. Res. Soc..

[37]  Hongqi Li,et al.  Fuzzy Multi Attribute Assessment Model for Software Outsourcing Partnership Formation , 2018, IEEE Access.

[38]  Michael Daskalantonakis,et al.  Achieving higher SEI levels , 1994, IEEE Software.

[39]  Sen Guo,et al.  Fuzzy best-worst multi-criteria decision-making method and its applications , 2017, Knowl. Based Syst..

[40]  PriyadarshineePragati,et al.  Understanding and predicting the determinants of cloud computing adoption , 2017 .

[41]  Vahid Garousi,et al.  When and what to automate in software testing? A multi-vocal literature review , 2016, Inf. Softw. Technol..

[42]  Arumugam Seetharaman,et al.  The usage and adoption of cloud computing by small and medium businesses , 2013, Int. J. Inf. Manag..

[43]  Siffat Ullah Khan,et al.  An Evaluation Framework for Communication and Coordination Processes in Offshore Software Development Outsourcing Relationship: Using Fuzzy Methods , 2019, IEEE Access.

[44]  Arun Kumar Sangaiah,et al.  An exploration of FMCDM approach for evaluating the outcome/success of GSD projects , 2013 .

[45]  John Grundy,et al.  A systematic mapping study of mobile application testing techniques , 2016, J. Syst. Softw..

[46]  Ossi Taipale,et al.  Adoption and use of cloud-based testing in practice , 2014, Software Quality Journal.