A Neuro-Fuzzy Method to Improving Backfiring Conversion Ratios

TL;DR

This paper presents a hybrid Neuro-Fuzzy approach to enhance the accuracy of backfiring conversion ratios between function points and source lines of code, addressing high error margins in traditional methods.

Contribution

It introduces a novel Neuro-Fuzzy based method that improves backfiring accuracy and compares its performance against standard conversion ratios.

Findings

The Neuro-Fuzzy method outperforms traditional backfiring ratios.

Improved conversion accuracy reduces estimation errors.

The approach combines neural learning with fuzzy reasoning for better results.

Abstract

Software project estimation is crucial aspect in delivering software on time and on budget. Software size is an important metric in determining the effort, cost, and productivity. Today, source lines of code and function point are the most used sizing metrics. Backfiring is a well-known technique for converting between function points and source lines of code. However when backfiring is used, there is a high margin of error. This study introduces a method to improve the accuracy of backfiring. Intelligent systems have been used in software prediction models to improve performance over traditional techniques. For this reason, a hybrid Neuro-Fuzzy is used because it takes advantages of the neural networks learning and fuzzy logic human-like reasoning. This paper describes an improved backfiring technique which uses Neuro-Fuzzy and compares the new method against the default conversion ratios currently used by software practitioners.