Applying Fuzzy ID3 Decision Tree for Software Effort Estimation

TL;DR

This paper explores the use of a fuzzy ID3 decision tree model for more accurate software effort estimation by handling uncertain data, demonstrating improved prediction accuracy over traditional methods.

Contribution

It introduces a fuzzy ID3 decision tree model that integrates fuzzy set theory with ID3 for better handling of imprecise data in software effort estimation.

Findings

Fuzzy ID3 outperforms crisp ID3 in prediction accuracy.

The model effectively manages uncertain and imprecise project data.

Experimental results show improved MMRE and Pred metrics.

Abstract

Web Effort Estimation is a process of predicting the efforts and cost in terms of money, schedule and staff for any software project system. Many estimation models have been proposed over the last three decades and it is believed that it is a must for the purpose of: Budgeting, risk analysis, project planning and control, and project improvement investment analysis. In this paper, we investigate the use of Fuzzy ID3 decision tree for software cost estimation; it is designed by integrating the principles of ID3 decision tree and the fuzzy set-theoretic concepts, enabling the model to handle uncertain and imprecise data when describing the software projects, which can improve greatly the accuracy of obtained estimates. MMRE and Pred are used as measures of prediction accuracy for this study. A series of experiments is reported using two different software projects datasets namely, Tukutuku and COCOMO'81 datasets. The results are compared with those produced by the crisp version of the ID3 decision tree.