<abstract xmlns="http://www.w3.org/1999/xhtml"><p>During the recent years, numerous endeavours have been made in the area of software development effort estimation for calculating the software costs in the preliminary development stages. These studies have resulted in the offering of a great many of the models. Despite the large deal of efforts, the substantial problems of the offered methods are their dependency on the used data collection and, sometimes, their lack of appropriate efficiency. The current article attempts to present a model for software development effort estimation through making use of evolutionary algorithms and neural networks. The distinctive characteristic of this model is its lack of dependency on the collection of data used as well as its high efficiency. To evaluate the proposed model, six different data collections have been used in the area of software effort estimation. The reason for the application of several data collections is related to the investigation of the model performance independence of the data collection used. The evaluation scales have been MMRE, MdMRE and PRED (0.25). The results have indicated that the proposed model, besides delivering high efficiency in contrast to its counterparts, produces the best responses for all of the used data collections.</p></abstract>

During the recent years, numerous endeavours have been made in the area of software development effort estimation for calculating the software costs in the preliminary development stages. These studies have resulted in the offering of a great many of the models. Despite the large deal of efforts, the substantial problems of the offered methods are their dependency on the used data collection and, sometimes, their lack of appropriate efficiency. The current article attempts to present a model for software development effort estimation through making use of evolutionary algorithms and neural networks. The distinctive characteristic of this model is its lack of dependency on the collection of data used as well as its high efficiency. To evaluate the proposed model, six different data collections have been used in the area of software effort estimation. The reason for the application of several data collections is related to the investigation of the model performance independence of the data collection used. The evaluation scales have been MMRE, MdMRE and PRED (0.25). The results have indicated that the proposed model, besides delivering high efficiency in contrast to its counterparts, produces the best responses for all of the used data collections.

A Dataset-Independent Model for Estimating Software Development Effort Using Soft Computing Techniques

Computer Engineering Department, Kerman Branch

Computer Engineering Department, Bardsir Branch

Department of Energy Management and Optimization

Graduate University of Advanced Technology

Applied Computer Systems

This work is licensed under the Creative Commons Attribution 4.0 Public License.

During the recent years, numerous endeavours have been made in the area of software development effort estimation for calculating the software costs in the...

A Dataset-Independent Model for Estimating Software Development Effort Using Soft Computing Techniques

Published Online: Feb 20, 2020

Page range: 82 - 93

DOI: https://doi.org/10.2478/acss-2019-0011

Keywords
Clustering, estimation, feature selection, genetic algorithm, imperialist competitive algorithm, neural network, regression, software development effort

© 2019 Mahdi Khazaiepoor et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 Public License.

A Dataset-Independent Model for Estimating Software Development Effort Using Soft Computing Techniques

Published Online: Feb 20, 2020

Page range: 82 - 93

DOI: https://doi.org/10.2478/acss-2019-0011

KeywordsClustering, estimation, feature selection, genetic algorithm, imperialist competitive algorithm, neural network, regression, software development effort

© 2019 Mahdi Khazaiepoor et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 Public License.

Keywords
Clustering, estimation, feature selection, genetic algorithm, imperialist competitive algorithm, neural network, regression, software development effort