article

An empirical validation of a neural network model for software effort estimation

Authors:

Seung BaekAuthors Info & Claims

Expert Systems with Applications: An International Journal, Volume 35, Issue 3

Pages 929 - 937

https://doi.org/10.1016/j.eswa.2007.08.001

Published: 01 October 2008 Publication History

Abstract

As software becomes more complex and its scope dramatically increases, the importance of research on developing methods for estimating software development efforts has perpetually increased. Such accurate estimation has a prominent impact on the success of projects. Out of the numerous methods for estimating software development efforts that have been proposed, line of code (LOC)-based constructive cost model (COCOMO), function point-based regression model (FP), neural network model (NN), and case-based reasoning (CBR) are among the most popular models. Recent research has tended to focus on the use of function points (FPs) in estimating the software development efforts, however, a precise estimation should not only consider the FPs, which represent the size of the software, but should also include various elements of the development environment for its estimation. Therefore, this study is designed to analyze the FPs and the development environments of recent software development cases. The primary purpose of this study is to propose a precise method of estimation that takes into account and places emphasis on the various software development elements. This research proposes and evaluates a neural network-based software development estimation model.

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Cited By

Kumar KSrinivas K(2023)An accurate analogy based software effort estimation using hybrid optimization and machine learning techniquesMultimedia Tools and Applications10.1007/s11042-023-14522-x82:20(30463-30490)Online publication date: 23-Feb-2023
https://dl.acm.org/doi/10.1007/s11042-023-14522-x
Alhamed MStorer T(2021)Playing Planning Poker in CrowdsProceedings of the 43rd International Conference on Software Engineering10.1109/ICSE43902.2021.00014(1-12)Online publication date: 22-May-2021
https://dl.acm.org/doi/10.1109/ICSE43902.2021.00014
Suresh Kumar PBehera HK ANayak JNaik B(2020)Advancement from neural networks to deep learning in software effort estimationComputer Science Review10.1016/j.cosrev.2020.10028838:COnline publication date: 1-Nov-2020
https://dl.acm.org/doi/10.1016/j.cosrev.2020.100288
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Index Terms

An empirical validation of a neural network model for software effort estimation
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
  2. Modeling and simulation
    1. Model development and analysis
      1. Model verification and validation
2. Social and professional topics
  1. Professional topics
    1. Management of computing and information systems
      1. Implementation management
        Pricing and resource allocation
      2. Project and people management

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Published In

cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 35, Issue 3

October, 2008

942 pages

ISSN:0957-4174

Issue’s Table of Contents

Copyright © Elsevier Ltd © 2007.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 October 2008

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Cited By

Kumar KSrinivas K(2023)An accurate analogy based software effort estimation using hybrid optimization and machine learning techniquesMultimedia Tools and Applications10.1007/s11042-023-14522-x82:20(30463-30490)Online publication date: 23-Feb-2023
https://dl.acm.org/doi/10.1007/s11042-023-14522-x
Alhamed MStorer T(2021)Playing Planning Poker in CrowdsProceedings of the 43rd International Conference on Software Engineering10.1109/ICSE43902.2021.00014(1-12)Online publication date: 22-May-2021
https://dl.acm.org/doi/10.1109/ICSE43902.2021.00014
Suresh Kumar PBehera HK ANayak JNaik B(2020)Advancement from neural networks to deep learning in software effort estimationComputer Science Review10.1016/j.cosrev.2020.10028838:COnline publication date: 1-Nov-2020
https://dl.acm.org/doi/10.1016/j.cosrev.2020.100288
de Campos Souza PGuimaraes AAraujo VRezende TAraujo V(2019)Incremental regularized Data Density-Based Clustering neural networks to aid in the construction of effort forecasting systems in software developmentApplied Intelligence10.1007/s10489-019-01449-w49:9(3221-3234)Online publication date: 1-Sep-2019
https://dl.acm.org/doi/10.1007/s10489-019-01449-w
Gautam SSingh V(2018)The state‐of‐the‐art in software development effort estimationJournal of Software: Evolution and Process10.1002/smr.198330:12Online publication date: 12-Dec-2018
https://dl.acm.org/doi/10.1002/smr.1983
Çebi FSökmen N(2017)Decision-Tree Models for Predicting Time Performance in Software-Intensive ProjectsInternational Journal of Information Technology Project Management10.4018/IJITPM.20170401058:2(64-86)Online publication date: 1-Apr-2017
https://dl.acm.org/doi/10.4018/IJITPM.2017040105
Alelyani TMao KYang YTurhan BBowes DShihab E(2017)Context-Centric PricingProceedings of the 13th International Conference on Predictive Models and Data Analytics in Software Engineering10.1145/3127005.3127012(63-72)Online publication date: 8-Nov-2017
https://dl.acm.org/doi/10.1145/3127005.3127012
Badri MBadri LFlageol WToure F(2017)Source code size prediction using use case metricsInnovations in Systems and Software Engineering10.1007/s11334-016-0285-713:2-3(143-159)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1007/s11334-016-0285-7
Nassif AAzzeh MCapretz LHo D(2016)Neural network models for software development effort estimationNeural Computing and Applications10.1007/s00521-015-2127-127:8(2369-2381)Online publication date: 1-Nov-2016
https://dl.acm.org/doi/10.1007/s00521-015-2127-1
Whigham POwen CMacdonell S(2015)A Baseline Model for Software Effort EstimationACM Transactions on Software Engineering and Methodology10.1145/273803724:3(1-11)Online publication date: 13-May-2015
https://dl.acm.org/doi/10.1145/2738037
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