Neuro-Fuzzy Algorithmic (NFA) Models and Tools for Estimation

TL;DR

This paper introduces a neuro-fuzzy framework combining neural networks, fuzzy logic, and algorithmic models to improve the accuracy of various estimation tasks like cost, quality, and risk analysis.

Contribution

It presents a novel, patent-pending soft computing framework that enhances estimation accuracy across different domains by integrating neural, fuzzy, and algorithmic models.

Findings

NFA model outperforms standalone algorithmic models in accuracy

Validated using industrial software project data

Framework is adaptable to various estimation challenges

Abstract

Accurate estimation such as cost estimation, quality estimation and risk analysis is a major issue in management. We propose a patent pending soft computing framework to tackle this challenging problem. Our generic framework is independent of the nature and type of estimation. It consists of neural network, fuzzy logic, and an algorithmic estimation model. We made use of the Constructive Cost Model (COCOMO), Analysis of Variance (ANOVA), and Function Point Analysis as the algorithmic models and validated the accuracy of the Neuro-Fuzzy Algorithmic (NFA) Model in software cost estimation using industrial project data. Our model produces more accurate estimation than using an algorithmic model alone. We also discuss the prototypes of our tools that implement the NFA Model. We conclude with our roadmap and direction to enrich the model in tackling different estimation challenges.