Applying Absolute Residuals as Evaluation Criterion for Estimating the Development Time of Software Projects by Means of a Neuro-Fuzzy Approach

Noel García-Díaz,Juan Garcia-Virgen,Lilia Muñoz,Alberto Verduzo-Ramirez

doi:10.20897/lectito.201646

Noel García-Díaz, Juan Garcia-Virgen + Show 2 more

Open Access

https://doi.org/10.20897/lectito.201646

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Abstract

In the software development field, software practitioners expend between 30% and 40% more effort than is predicted. Accordingly, researchers have proposed new models for estimating the development effort such that the estimations of these models are close to actual ones. In this study, an application based on a new neuro-fuzzy system (NFS) is analyzed. The NFS accuracy was compared to that of a statistical multiple linear regression (MLR) model. The criterion for evaluating the accuracy of estimation models has mainly been the Magnitude of Relative Error (MRE), however, it was recently found that MRE is asymmetric, and the use of Absolute Residuals (AR) has been proposed, therefore, in this study, the accuracy results of the NFS and MLR were based on AR. After a statistical paired t-test was performed, results showed that accuracy of the New-NFS is statistically better than that of the MLR at the 99% confidence level. It can be concluded that a new-NFS could be used for predicting the effort of software development projects when they have been individually developed on a disciplined process.

Highlights

A high percentage of machine learning models have been proposed based on an accuracy asymmetric criterion Magnitude of Relative Error (MRE) (Wen et al, 2012), it was recently found that MRE is asymmetric and that the use of the Absolute Residual (AR) should be used instead because of AR is unbiased and it is does no lead to asymmetry (Shepperd & MacDonell, 2012)
The objective of this paper is to present a new Neuro-Fuzzy System (NFS) for achieving higher accuracy for estimating the development time of software projects using the AR and its mean (MAR)
By Wen et al, found eight types of machine learning techniques applied to Software Development Effort Estimation (SDEE); it was not found any paper using data from small projects and basing its conclusions on AR and having a neurofuzzy system (NFS) using a Grid partitioning method to obtain the parameters for input and output of the membership functions (MF)

Summary

INTRODUCTION

A high percentage of machine learning models have been proposed based on an accuracy asymmetric criterion Magnitude of Relative Error (MRE) (Wen et al, 2012), it was recently found that MRE is asymmetric and that the use of the Absolute Residual (AR) should be used instead because of AR is unbiased and it is does no lead to asymmetry (Shepperd & MacDonell, 2012). Software Engineering Management includes planning and measurement of SE, which involves to the Software Development Effort Estimation (SDEE). Estimation of software development effort is the basis for project bidding, budgeting and planning. Because that no single technique to estimate software development effort is best for all situations, it is important to propose new models to compare their results and generate more realistic estimates (Boehm & Abts, 2000). The objective of this paper is to present a new Neuro-Fuzzy System (NFS) for achieving higher accuracy for estimating the development time of software projects using the AR and its mean (MAR).

RELATED WORK

Evaluation criterion

Multiple linear regression

RESULTS

CONCLUSIONS AND FUTURE RESEARCH