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 neurofuzzy 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.

Two of the three most important causes of Information Technology projects failure have been related to a poor resource estimation. In average, software developers expend from 30% to 40% more effort than is estimated. 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 then generate more realistic estimates. In this study, the aimed is to present a hybrid model that combine fuzzy logic and neural networks for achieving higher accuracy for estimating the development time of software projects. The accuracy of time estimation for a Neuro-Fuzzy System (NFS) is statistically better than the accuracy obtained from a previous NFS and statistical regression (model most used by default to compare) when the forty-one modules developed from ten programs were used as dataset. Results show that the value of MMRE (Mean of Magnitude of Relative Error) applying a NFS was substantially lower than MMRE applying a previous NFS and statistical regression. It can be conclude that a new NFS could be applied for estimating the effort of software development projects when they have been individually developed on a disciplined process.