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Analyzing the interactions among factors affecting cloud adoption for software testing: a two-stage ISM-ANN approach

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Abstract

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 for Cloud-based testing adoption. Therefore, the objective is to develop a two-stage Interpretive Structural Model (ISM) and Artificial Neural Network (ANN)-based approach, for analyzing the factors influencing cloud adoption for software testing. This study first identifies the determinants and predictors of Cloud adoption for software testing through systematic literature (SLR) and empirical survey. Based on the collected data, an ANN was incorporated to weight the nonlinear effect of the predictors. Then, based on the results of empirical survey; a panel of ten experts was selected, to explore the multifaceted interrelationships among the influential factors (IFs) through SM. To provide a concise understanding of the facts, Cross-Impact Matrix Multiplication Applied to the Classification (MICMAC) was used for factors classification. To achieve our objective, through SLR this study identifies 70 IFs. To offer a brief understanding of the issue, we distributed the identified IFs into ten predictors and analyzed their nonlinear effect on the predictors through ANN. Based on the ANN outcomes, the foremost significant independent variable in predicting CCA intention of CC are perceived business concerns (100%), followed by feasibility planning and risk analysis (84.4%), perceived level of trust (76.1%). Finally, the key 44 factors, identified through panel review, were priorities through ISM and were distributed into four Quadrants using MICMAC approach. Some studies in the form of survey have been conducted to examine the IFs affecting CCA. However, no attempt was made to explore the multifaceted interrelationships among them. This study concludes that software testing should be carried out in the Cloud

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Notes

  1. www.soasta.com

  2. wwww.getzephyr.com

  3. www.sogeti.com/Staas

  4. wwww.skytap.com

  5. http://saucelabs.com/

  6. www-935.ibm.com/services/us/en/it-services

  7. www.utest.com

  8. www.pushtotest.com

  9. http://blazemeter.com

  10. www.csscorp.com/product-lifecycle-services/

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Acknowledgements

This research was supported by Taif University Researchers Supporting Project number (TURSP-2020/314), Taif University, Taif, Saudi Arabia.

Funding

This research was supported by Taif University Researchers Supporting Project number (TURSP-2020/314), Taif University, Taif, Saudi Arabia.

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Authors and Affiliations

  1. Department of Computer Science and Technology, China University of Petroleum-Beijing, Beijing, 102249, China

    Sikandar Ali & Jiwei Huang

  2. Department of Information Technology, The University of Haripur, Haripur, 22620, Pakistan

    Sikandar Ali

  3. Department of Computer System Engineering, University of Engineering and Technology, Peshawar, 25000, Pakistan

    Samad Baseer

  4. Department of Computer Science, Faculty of Science and Arts at Belqarn, University of Bisha, Bisha, 61985, Saudi Arabia

    Irshad Ahmed Abbasi

  5. Department of Computer Science, College of Computers and Information Technology, Taif University, P. O. Box 11099, Taif, 21944, Saudi Arabia

    Bader Alouffi & Wael Alosaimi

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  1. Sikandar Ali
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  3. Irshad Ahmed Abbasi
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  4. Bader Alouffi
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First author contributes to conceptualization, methodology, data curation, visualization, investigation, and writing. Rest of the authors do supervision, reviewing, and revisioning.

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Appendices

Appendices

1.1 Appendix-A

Level of partitions of factors are presented in Table and can be downloaded from https://drive.google.com/file/d/1lPuvVomEuR1S-t0QA1EBna0QD7fsNElo/view?usp=sharing

1.2 Appendix-B

Items for measuring factors and predictors are presented in Table and can be downloaded from https://drive.google.com/open?id=1FXX79F1TsPB5o-CDYNJ58SdVQbsVL4OR

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Ali, S., Baseer, S., Abbasi, I.A. et al. Analyzing the interactions among factors affecting cloud adoption for software testing: a two-stage ISM-ANN approach. Soft Comput 26, 8047–8075 (2022). https://doi.org/10.1007/s00500-022-07062-3

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