review-article

Advancement from neural networks to deep learning in software effort estimation: : Perspective of two decades

Authors:

P. Suresh Kumar,

Anisha Kumari K,

Janmenjoy Nayak,

Bighnaraj NaikAuthors Info & Claims

Volume 38, Issue C

https://doi.org/10.1016/j.cosrev.2020.100288

Published: 01 November 2020 Publication History

Abstract

In the software engineering, estimation of the effort, time and cost required for the development of software projects is an important issue. It is a very difficult task for project managers to predict the cost and effort needed in the premature stages of planning. Software estimation ahead of development can reduce the risk and increase the success rate of the project. Many traditional and machine learning methods are used for software effort estimation by researchers, but always it has been a challenge to predict the effort accurately. In this study, different Artificial Neural Network (ANN) used for effort estimation is discussed. It is observed that the prediction of software effort by using ANN is more precise and better compared to traditional methods such as Function point, Use-case methods and COCOMO etc. Models based on neural networks are competitive in nature as compared to statistical and traditional regression methods. This paper explains the overview of various ANN such as basic NN, higher order NN, and deep learning networks used by the researchers for software effort estimation.

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Cited By

Mustafa EOsman R(2024)A random forest model for early-stage software effort estimation for the SEERA datasetInformation and Software Technology10.1016/j.infsof.2024.107413169:COnline publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.infsof.2024.107413
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https://dl.acm.org/doi/10.1007/s10586-024-04418-2
Azzeh MAlkhateeb ABou Nassif A(2024)Software effort estimation using convolutional neural network and fuzzy clusteringNeural Computing and Applications10.1007/s00521-024-09855-z36:23(14449-14464)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1007/s00521-024-09855-z

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cover image Computer Science Review

Computer Science Review Volume 38, Issue C

Nov 2020

528 pages

ISSN:1574-0137

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Published: 01 November 2020

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Cited By

Mustafa EOsman R(2024)A random forest model for early-stage software effort estimation for the SEERA datasetInformation and Software Technology10.1016/j.infsof.2024.107413169:COnline publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.infsof.2024.107413
Manchala PBisi M(2024)TSoptEE: two-stage optimization technique for software development effort estimationCluster Computing10.1007/s10586-024-04418-227:7(8889-8908)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1007/s10586-024-04418-2
Azzeh MAlkhateeb ABou Nassif A(2024)Software effort estimation using convolutional neural network and fuzzy clusteringNeural Computing and Applications10.1007/s00521-024-09855-z36:23(14449-14464)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1007/s00521-024-09855-z
Hassanali MSoltanaghaei MJavdani Gandomani TZamani Boroujeni F(2024)Software development effort estimation using boosting algorithms and automatic tuning of hyperparameters with OptunaJournal of Software: Evolution and Process10.1002/smr.266536:9Online publication date: 16-Sep-2024
https://dl.acm.org/doi/10.1002/smr.2665

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