Fuzzy Model Tree For Early Effort Estimation

TL;DR

This paper explores using a Fuzzy Model Tree to improve early software effort estimation based on Use Case Points, showing promising results over traditional methods and other machine learning models.

Contribution

It introduces a novel Fuzzy Model Tree approach for effort estimation from UCP, outperforming classical and some machine learning models.

Findings

Fuzzy Model Tree outperforms classical UCP and Linear Regression.

The approach shows better accuracy than Treeboost.

Results indicate promising potential for early effort estimation.

Abstract

Use Case Points (UCP) is a well-known method to estimate the project size, based on Use Case diagram, at early phases of software development. Although the Use Case diagram is widely accepted as a de-facto model for analyzing object oriented software requirements over the world, UCP method did not take sufficient amount of attention because, as yet, there is no consensus on how to produce software effort from UCP. This paper aims to study the potential of using Fuzzy Model Tree to derive effort estimates based on UCP size measure using a dataset collected for that purpose. The proposed approach has been validated against Treeboost model, Multiple Linear Regression and classical effort estimation based on the UCP model. The obtained results are promising and show better performance than those obtained by classical UCP, Multiple Linear Regression and slightly better than those obtained by Tree boost model.