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