research-article

Comparison of regression model, feed-forward neural network and radial basis neural network for software development effort estimation

Authors:

Vachik S. Dave,

Kamlesh DuttaAuthors Info & Claims

ACM SIGSOFT Software Engineering Notes, Volume 36, Issue 5

Pages 1 - 5

https://doi.org/10.1145/2020976.2020982

Published: 30 September 2011 Publication History

Abstract

Accurate estimation of software development parameters such as effort, cost, and schedule is very important for effectively managing software development projects. Several software development effort estimation models have been developed in the last few decades. Determining, which is the best estimation model is difficult to decide for a software management team. In this paper we have compared Neural Network models and regression model for software development effort estimation. The comparison reveals that the Neural Network (NN) is better for effort prediction compared to regression analysis model. Further, we have compared two Neural Network models - Feed-Forward Neural Network (FFNN) and Radial Basis Neural Network (RBNN). The evaluation of the models is based on Mean Magnitude Relative Error (MMRE), Relative Standard Deviation (RSD) and Root Mean Squared Error (RMSE). The experimental results show that the RBNN model exhibits better prediction ability than FFNN.

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Cited By

Boujida FAmazal FIdri A(2025)Neural Networks‐Based Software Development Effort EstimationJournal of Software: Evolution and Process10.1002/smr.275637:2Online publication date: 6-Feb-2025
https://dl.acm.org/doi/10.1002/smr.2756
Dutta KGupta VDave V(2022)Analysis and Comparison of Neural Network Models for Software Development Effort EstimationResearch Anthology on Agile Software, Software Development, and Testing10.4018/978-1-6684-3702-5.ch009(165-193)Online publication date: 2022
https://doi.org/10.4018/978-1-6684-3702-5.ch009
Suresh Kumar PBehera HNayak JNaik B(2022)A pragmatic ensemble learning approach for effective software effort estimationInnovations in Systems and Software Engineering10.1007/s11334-020-00379-y18:2(283-299)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1007/s11334-020-00379-y
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Comparison of regression model, feed-forward neural network and radial basis neural network for software development effort estimation

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cover image ACM SIGSOFT Software Engineering Notes

ACM SIGSOFT Software Engineering Notes Volume 36, Issue 5

September 2011

160 pages

ISSN:0163-5948

DOI:10.1145/2020976

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Copyright © 2011 Authors.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 September 2011

Published in SIGSOFT Volume 36, Issue 5

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Cited By

Boujida FAmazal FIdri A(2025)Neural Networks‐Based Software Development Effort EstimationJournal of Software: Evolution and Process10.1002/smr.275637:2Online publication date: 6-Feb-2025
https://dl.acm.org/doi/10.1002/smr.2756
Dutta KGupta VDave V(2022)Analysis and Comparison of Neural Network Models for Software Development Effort EstimationResearch Anthology on Agile Software, Software Development, and Testing10.4018/978-1-6684-3702-5.ch009(165-193)Online publication date: 2022
https://doi.org/10.4018/978-1-6684-3702-5.ch009
Suresh Kumar PBehera HNayak JNaik B(2022)A pragmatic ensemble learning approach for effective software effort estimationInnovations in Systems and Software Engineering10.1007/s11334-020-00379-y18:2(283-299)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1007/s11334-020-00379-y
Duong NTran K(2022)Estimation of seepage velocity and piping resistance of fiber-reinforced soil by using artificial neural network-based approachNeural Computing and Applications10.1007/s00521-022-07708-135:3(2443-2455)Online publication date: 28-Aug-2022
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Skiba MDutka BMłynarczuk M(2021)MLP-Based Model for Estimation of Methane Seam PressureEnergies10.3390/en1422766114:22(7661)Online publication date: 16-Nov-2021
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Dutta KGupta VDave V(2019)Analysis and Comparison of Neural Network Models for Software Development Effort EstimationJournal of Cases on Information Technology10.4018/JCIT.201904010621:2(88-112)Online publication date: Apr-2019
https://doi.org/10.4018/JCIT.2019040106
Sehra SBrar YKaur NSehra S(2019)Software effort estimation using FAHP and weighted kernel LSSVM machineSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-018-3639-223:21(10881-10900)Online publication date: 1-Nov-2019
https://dl.acm.org/doi/10.1007/s00500-018-3639-2
Basar A(2019)An Expert System Methodology for Planning IT Projects with Hesitant Fuzzy Effort: An ApplicationIndustrial Engineering in the Big Data Era10.1007/978-3-030-03317-0_1(3-18)Online publication date: 24-Jan-2019
https://doi.org/10.1007/978-3-030-03317-0_1
(2017)Research patterns and trends in software effort estimationInformation and Software Technology10.1016/j.infsof.2017.06.00291:C(1-21)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1016/j.infsof.2017.06.002
Sharma DChandra P(2017)Applicability of Soft Computing and Optimization Algorithms in Software Testing and Metrics – A Brief ReviewProceedings of the Eighth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016)10.1007/978-3-319-60618-7_53(535-546)Online publication date: 19-Aug-2017
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