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

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 (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.

Knowledge Management System Adoption to Improve Decision-Making Process in Higher Learning Institutions in the Developing Countries: A Conceptual Framework

“…In the context of educational institutions, adopting KMS can minimize the education demand-supply gap [6,7], and such notion has resulted in heightened awareness and investment in KMS innovation in the majority of nations to enhance their system of education [8,9]. Additionally, KMS adoption that constitutes the education provision has been considered a set of processes to be implemented to enhance the effectiveness of HLI in terms of its performance and objectives achievement.…”

Notice of Retraction: Infrared Sensing Based Non-Invasive Initial Diagnosis of Chronic Liver Disease Using Ensemble Learning

“…1) Smaller database restricts the performance of classifiers 2) Fewer data features result in loss of information 3) Ability to deal with inter-patient variability 4) Higher computational cost 5) Higher execution time The proposed work has incorporated a comparatively new AI approach, known as the ensemble learning method, to overcome the limitations mentioned above. Various research has been published that involves ensemble learning method, and this approach has been successful in getting rid of the limitation associated with conventional Machine Learning (ML) models [18]- [24]. Further, data type and its features also play a vital role in defining the classifier's performance.…”

Code review effectiveness: an empirical study on selected factors influence

“…Specifically, it would be possible to classify the barriers in different categories and to suggest a robust framework aiming at supporting decision making in the field of code reviews process design as it is often presented in other areas e.g. [46, 47] or [13].…”