Regularized Fuzzy Neural Networks to Aid Effort Forecasting in the Construction and Software Development

TL;DR

This paper introduces a hybrid fuzzy neural network model to improve effort estimation in software development and construction by providing interpretable, rule-based predictions that account for project complexity.

Contribution

It presents a novel regularized fuzzy neural network approach that combines neural and fuzzy systems for more accurate effort forecasting in complex projects.

Findings

Model showed promising results on real data

Enhanced interpretability of effort estimates

Potential to improve project management decisions

Abstract

Predicting the time to build software is a very complex task for software engineering managers. There are complex factors that can directly interfere with the productivity of the development team. Factors directly related to the complexity of the system to be developed drastically change the time necessary for the completion of the works with the software factories. This work proposes the use of a hybrid system based on artificial neural networks and fuzzy systems to assist in the construction of an expert system based on rules to support in the prediction of hours destined to the development of software according to the complexity of the elements present in the same. The set of fuzzy rules obtained by the system helps the management and control of software development by providing a base of interpretable estimates based on fuzzy rules. The model was submitted to tests on a real database, and its results were promissory in the construction of an aid mechanism in the predictability of the software construction.