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An empirical validation of software cost estimation models

Author:

Chris F KemererAuthors Info & Claims

Communications of the ACM, Volume 30, Issue 5

Pages 416 - 429

https://doi.org/10.1145/22899.22906

Published: 01 May 1987 Publication History

Abstract

Practitioners have expressed concern over their inability to accurately estimate costs associated with software development. This concern has become even more pressing as costs associated with development continue to increase. As a result, considerable research attention is now directed at gaining a better understanding of the software-development process as well as constructing and evaluating software cost estimating tools. This paper evaluates four of the most popular algorithmic models used to estimate software costs (SLIM, COCOMO, Function Points, and ESTIMACS). Data on 15 large completed business data-processing projects were collected and used to test the accuracy of the models' ex post effort estimation. One important result was that Albrecht's Function Points effort estimation model was validated by the independent data provided in this study [3]. The models not developed in business data-processing environments showed significant need for calibration. As models of the software-development process, all of the models tested failed to sufficiently reflect the underlying factors affecting productivity. Further research will be required to develop understanding in this area.

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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
Mohammad Ayub Latif Khan MAkbar SKhan U(2024)Investigating Effort Estimation Techniques for Mobile Applications: An Efficient ApproachVFAST Transactions on Software Engineering10.21015/vtse.v12i4.201812:4(267-281)Online publication date: 31-Dec-2024
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An empirical validation of software cost estimation models

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Published In

cover image Communications of the ACM

Communications of the ACM Volume 30, Issue 5

May 1987

93 pages

ISSN:0001-0782

EISSN:1557-7317

DOI:10.1145/22899

Editor:
Peter J. Denning
NASA Ames Research Center, Moffett Field, CA

Issue’s Table of Contents

Copyright © 1987 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 May 1987

Published in CACM Volume 30, 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
Mohammad Ayub Latif Khan MAkbar SKhan U(2024)Investigating Effort Estimation Techniques for Mobile Applications: An Efficient ApproachVFAST Transactions on Software Engineering10.21015/vtse.v12i4.201812:4(267-281)Online publication date: 31-Dec-2024
https://doi.org/10.21015/vtse.v12i4.2018
Liu FMcKinnon T(2024)Organizational Ohm's Law in Practice: Measuring Engineering ProductivityIEEE Transactions on Engineering Management10.1109/TEM.2024.342189271(11494-11504)Online publication date: 2024
https://doi.org/10.1109/TEM.2024.3421892
Gupta RDumka AMazumdar B(2024)Software Cost Estimation: A Comparative Analysis2024 International Conference on Computer, Electrical & Communication Engineering (ICCECE)10.1109/ICCECE58645.2024.10497286(1-8)Online publication date: 2-Feb-2024
https://doi.org/10.1109/ICCECE58645.2024.10497286
Javdani Gandomani TDashti MZulzalil HSultan A(2024)Enhancing Software Effort Estimation in the Analogy-Based Approach Through the Combination of Regression MethodsIEEE Access10.1109/ACCESS.2024.348082912(152122-152137)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3480829
Kaushik ASheoran KKapur RBhutani NSingh BSharma H(2024)SENSE: software effort estimation using novel stacking ensemble learningInnovations in Systems and Software Engineering10.1007/s11334-024-00581-2Online publication date: 14-Sep-2024
https://doi.org/10.1007/s11334-024-00581-2
Rankovic NRanković DIvanovic MLazić LRankovic NRanković DIvanovic MLazić L(2024)Optimizing Effort and Cost Estimation: Model Implementation Using Artificial Neural Networks and Taguchi’s Orthogonal Vector PlansRecent Advances in Artificial Intelligence in Cost Estimation in Project Management10.1007/978-3-031-76572-8_9(291-417)Online publication date: 7-Dec-2024
https://doi.org/10.1007/978-3-031-76572-8_9
Ali SRen JZhang KWu JLiu C(2023)Heterogeneous Ensemble Model to Optimize Software Effort Estimation AccuracyIEEE Access10.1109/ACCESS.2023.325653311(27759-27792)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3256533
Alvarez-Rodríguez JMendieta RCibrián ELlorens J(2023)Towards a method to quantitatively measure toolchain interoperability in the engineering lifecycleComputer Standards & Interfaces10.1016/j.csi.2023.10374486:COnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.csi.2023.103744
Abnane IIdri AChlioui IAbran A(2023)Evaluating ensemble imputation in software effort estimationEmpirical Software Engineering10.1007/s10664-022-10260-028:2Online publication date: 15-Mar-2023
https://dl.acm.org/doi/10.1007/s10664-022-10260-0
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