Neuro-Fuzzy Algorithmic (NFA) Models and Tools for Estimation
This addresses estimation challenges in management, particularly for software cost estimation, but appears incremental as it builds on existing algorithmic models like COCOMO.
The paper tackled the problem of accurate estimation in management, such as cost and risk analysis, by proposing a Neuro-Fuzzy Algorithmic (NFA) model that combines neural networks, fuzzy logic, and algorithmic models, and validated it on industrial software project data to show it produces more accurate estimates than using algorithmic models alone.
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.