Electrical and Computer Engineering Publications

Title

Improving the Performance of Neuro-Fuzzy Function Point Backfiring Model with Additional Environmental Factors

Document Type

Book Chapter

Publication Date

2014

First Page

260

URL with Digital Object Identifier

DOI: 10.4018/978-1-4666-4785-5.ch014

Last Page

280

Abstract

Backfiring is a technique used for estimating the size of source code based on function points and programming. In this study, additional software environmental parameters such as Function Point count standard, development environment, problem domain and size are applied to the Neuro-Fuzzy Function Point Backfiring (NFFPB) model. The neural network and fuzzy logic designs are introduced for both models. Both estimation models are compared against the same data source of software projects. It was found that the original NFFPB model out performs the extended model. The results were investigated and explained to why the extended model performed worse.