A Comparison Between Decision Trees and Decision Tree Forest Models for Software Development Effort Estimation
This addresses the challenge of accurate effort estimation for software practitioners and project managers, but it is incremental as it compares existing methods.
The paper compared decision tree forest (DTF) models to traditional decision trees and multiple linear regression for software effort estimation, finding that DTF is competitive and can serve as an alternative method.
Accurate software effort estimation has been a challenge for many software practitioners and project managers. Underestimation leads to disruption in the projects estimated cost and delivery. On the other hand, overestimation causes outbidding and financial losses in business. Many software estimation models exist; however, none have been proven to be the best in all situations. In this paper, a decision tree forest (DTF) model is compared to a traditional decision tree (DT) model, as well as a multiple linear regression model (MLR). The evaluation was conducted using ISBSG and Desharnais industrial datasets. Results show that the DTF model is competitive and can be used as an alternative in software effort prediction.