A Comparison Between Decision Trees and Decision Tree Forest Models for Software Development Effort Estimation

TL;DR

This paper compares decision tree forest models with traditional decision trees and linear regression for software effort estimation, demonstrating that DTF models are a competitive alternative based on evaluations with industrial datasets.

Contribution

It introduces a comparison of decision tree forest models with traditional models for effort estimation, highlighting the effectiveness of DTF as an alternative.

Findings

DTF model is competitive with traditional models

Evaluation on ISBSG and Desharnais datasets supports DTF's effectiveness

Decision tree forest models can improve effort estimation accuracy

Abstract

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.