Optimal resource selection for Green Software Development using Machine Learning

Abstract

Today software development consumes a lot of natural resources which are needed to be preserved for future needs. The resources that are used in developing software are huge in numbers casting a negative impact on the environment. Hence, one needs to utilize these resources in an efficient manner in order to conserve it. Since resources are limited, there is a need for more improved software as well as an efficient software development process which consumes less energy and resources. In order to fulfill this objective, Green Software Development (GSD) can be useful. But sometimes the cost incurred for the GSD may be too high and benefits obtained may be very less or negligible. This outcome may not be very beneficial to the developers. Therefore, this article proposes an effective approach using machine learning for cost-benefit analysis to provide optimal resource selection for GSD. This approach makes a trade-off between requirements and expenditures (cost incurred to achieve the objective based on the requirements) to provide optimal resource selection and aids in analyzing the economic feasibility for GSD.