Task Allocation in Distributed Agile Software Development using Machine Learning Approach

Abstract

In the 21st century, agile software development (ASD) has emerged as one of the prominent software development techniques. Every major global company has moved to ASD as a means of reducing costs. In pursuit of huge markets and cheap cost of labour, the industry has shifted to a Distributed Agile Software Development (DASD) environment. As a consequence of improper job allocation, clients may refuse to accept the project, team members may be demonized, and the project may collapse. Numerous scholars have spent the past decade researching different techniques for work allocation in Distributed Agile settings, and the results have been promising. Ontologies and Bayesian networks were among the techniques they employed. This is a list of brute force techniques that may be useful in certain situations. Additionally, these methods have not been used to distributed Agile software development job allocation. The purpose of this article is to design and implement a method for job allocation in distributed Agile software development that is based on machine learning. The findings indicate that the suggested model is more accurate in terms of task assignment.