Risk Prediction with Regression in Global Software Development using Machine Learning Approach: A Comparison of Linear and Decision Tree Regression

Abstract

Software development through teams at different geographical locations is a trend of modern era, which is not only producing good results without costing lot of money, but also productive in relation to its cost with low risk and high return. This shift of perception of working in a group rather than alone is getting stronger day by day and has become an important planning tool and part of their business strategy. Due to this phenomenal shift the development processes have become complex and chances of risks have been increased. The utilization of Machine learning to manage risk is helpful when taking care of and evaluating data. In this research regression approaches like Linear Regression and Tree Regression have been implemented to predict the responses of risks involved in global software development. Comparative analysis has also been performed between these two algorithms to determine the highest accuracy algorithms. The results indicate that Fine tree regression, which is one of techniques of decision tree regression, gave better results in terms of goodness of fit measures as compared to linear regression model fitted to examine the relationship of cost, time and resource related risk with the overall risk of global software development projects.