A Hybrid Intelligent Model for Software Cost Estimation

TL;DR

This paper proposes a hybrid neuro-fuzzy model to improve software cost estimation accuracy, effectively handling uncertain inputs and demonstrating an 18% improvement over existing methods.

Contribution

It introduces a novel hybrid neuro-fuzzy approach that enhances software effort prediction accuracy compared to traditional models.

Findings

Achieved 18% improvement in estimation accuracy

Demonstrated effectiveness on published and industrial data

Improved handling of uncertain and imprecise inputs

Abstract

Accurate software development effort estimation is critical to the success of software projects. Although many techniques and algorithmic models have been developed and implemented by practitioners, accurate software development effort prediction is still a challenging endeavor in the field of software engineering, especially in handling uncertain and imprecise inputs and collinear characteristics. In this paper, a hybrid in-telligent model combining a neural network model integrated with fuzzy model (neuro-fuzzy model) has been used to improve the accuracy of estimating software cost. The performance of the proposed model is assessed by designing and conducting evaluation with published project and industrial data. Results have shown that the proposed model demonstrates the ability of improving the estimation accuracy by 18% based on the Mean Magnitude of Relative Error (MMRE) criterion.