Investigating the use of random forest in software effort estimation

Over the last two decades, there has been an important increase in studies dealing with the software development effort estimation (SDEE) using machine learning (ML) techniques that aimed to improve the accuracy of the estimates and to understand the process used to generate these estimates. Among these ML techniques, decision tree-based models have received a considerable scholarly attention thanks to their generalization ability and...