Scrum based scaling using agile method to test software projects and its future solutions using in artificial neural networks

Abstract

Nowadays the software development steps are evident and unavoidable in developing software projects. Every day software demands have has been growing in this field, provides new innovative ideas and support to incorporate the customer needs in software development. In this paper Agile methods with scalability have been focused which are very important to pre-planning software development process and business cost estimation. It has not provided an optimal solution to all projects in past history. So, in this paper, scrum procedure is applied which is based on Agile methods with scalability projects, and are estimated by some of the criteria such as function point, use case point, object point, and storyboard points based effort estimation parameters similar to the waterfall model, spiral model, and rapid development model. But none of them gives an accurate result. The main reason for most of the projects failure is because of inaccurate estimation. So scrum-based agile method with extended version providing reasonable accurate result in developed software projects is considered which is estimated using various metrics. Scrum procedure with Agile method is used on different projects. It is constructed based on an agile framework. Its categories are estimated in the machine learning procedure, whose results are meted based on different types such as small projects, medium projects, and large projects which are estimated in extended versions of scrum based Agile methods. They construe 70% of software based on agile methods. Its estimation results are justified in the machine learning processes such as Bayesian regression using back propagation neural network.