SaaS reusability assessment using machine learning techniques

Abstract

With the aid of internet, cloud computing offers hardware and software resources as cloud service. Cloud computing is developing as an effective criterion for reusing. Software-as-a-service (SaaS) is one of the three cloud deployment models that provide on-demand software on a charge basis. A reusability model for SaaS needs to be developed in order to increase its benefits and effectively help end users. A product’s ability to be reused is essential to its easy and effective development. We have presented a software reusability estimation model in this paper. We have assessed the SaaS reusability using machine learning techniques such as adaptive neuro-fuzzy inference system (ANFIS), linear regression, support vector machine (SVM), ensemble, and neural networks. We compared machine learning models using commonality, accessibility, availability, customizability, and efficiency, as the SaaS reusability criteria. The root mean squared error (RMSE), mean squared error (MSE), and mean absolute error (MAE) are used to validate the findings from recommended methodologies with the required level of accuracy. The evaluation's findings have shown that machine learning algorithms yield estimations with a better degree of accuracy, making them more advantageous and practical for SaaS service providers as well as customers.