research-article

Simple empirical software effort estimation model

Authors:

Brad ClarkAuthors Info & Claims

ESEM '14: Proceedings of the 8th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement

Article No.: 43, Pages 1 - 4

https://doi.org/10.1145/2652524.2652558

Published: 18 September 2014 Publication History

Abstract

Context: An effort estimation model with more than 20 parameters is not very useful at early conceptual phase if you don't have a logical approach for specifying the input values.

Goal: This paper presents a simple approach for predicting software development effort.

Method: The regression model uses product size and application types to predict effort. Product size is measured in terms of the equivalent source lines of code. The analysis is based on empirical data collected from 317 very recent projects implemented within the United States Department of Defense over the course of nine years beginning in 2004.

Result: Statistical results showed that source lines of code and application type are significant contributors to development effort.

Conclusion: The equation is simpler and more viable to use for early estimates than traditional parametric cost models. The effect of product size on software effort shall be interpreted along with application domain.

References

[1]

Boehm B., Software Engineering Economics. Englewood Cliffs, NJ, Prentice-Hall, 1981

Digital Library

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Boehm B., Abts C., Brown W., Chulani S., Clark B., Horowitz E., Madachy R., Reifer D., Steece B., Software Cost Estimation with COCOMO II, Prentice-Hall, 2000

Digital Library

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Boehm, B., 2000, "Safe and simple software cost analysis," Software, IEEE, 17(5), pp. 14--17.

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Clark, B., Devnani-Chulani, S., and Boehm, B., 1998, "Calibrating the COCOMO II Post-Architecture model," Proc. Int'l Conf. Software Eng. (ICSE '98), pp. 477--480.

Digital Library

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Clark, B. K., 1997, "The Effects of Software Process Maturity on Software Development Effort," Technical Report No. AAT 9816016, University of Southern California, CA.

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Galorath, 2001, SEER-SEM^#8482; User's Manual, Galorath, Inc., El Segundo, CA, Chaps.1--4, 6--6, 15--6.

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Jones C., McGarry J., Dean J., Rosa W., Madachy, R., Boehm B., Clark B., Tan T., 2013, "Software Cost Metrics Manual" http://softwarecost.org/index.php?title=Main_Page, Site accessed on 27 February 2014.

[8]

Madachy, R.; Boehm, B.; Clark, B.; Tan, T.; Rosa, W. "US DoD Application Domain Empirical Software Cost Analysis", Empirical Software Engineering and Measurement (ESEM), 2011 International Symposium on, On page(s): 392--395

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Lipkin, I., 2011, "Test Software Development Project Productivity Model", Dissertation Thesis, Univerity of Toledo (Toledo, OH, 2011).

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QSM, Inc., 2001, User's Guide to SLIM-Estimate 5.0, 2nd Edition, QSM, Inc., McLean,VA, pp. 1--360.

[12]

Rosa W., Boehm B., Clark B., Madachy R., Dean J., 2013, "Domain-Driven Software Cost and Schedule Estimating Models: Using Software Resource Data Reports (SRDRs)", 2013 International Cost Estimating and Analysis Association (ICEAA), Professional Development & Training Workshop

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Rosa W., Packard T., Krupanand A., James Bilbro J., Hodal M.: COTS integration and estimation for ERP. Journal of Systems and Software 86(2): 538--550 (2013)

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Software Resource Data Report, 2011, http://dcarc.cape.osd.mil/Files/Policy/2011-SRDRFinal.pdf

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Tan, T, "Domain-Based Effort Distribution Model for Software Cost Estimation," PhD Dissertation, Computer Science Department, University of Southern California, June 20

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TECOLOTE, Inc., "Automated Cost Estimating Integrated Tools: CO$TAT", http://ww.aceit.com/Pages/Products/ProductPage.aspx?id=f6 38a6d8-60e9-414a-9970-7fed249b9d25, Site accessed on 1 July 2014.

Cited By

Long DDrylie SRitschel JKoschnick C(2024)An Assessment of Rules of Thumb for Software Phase Management, and the Relationship Between Phase Effort and Schedule SuccessIEEE Transactions on Software Engineering10.1109/TSE.2023.333938350:2(209-219)Online publication date: Feb-2024
https://doi.org/10.1109/TSE.2023.3339383
Rosa WClark BMadachy RBoehm B(2022)Empirical Effort and Schedule Estimation Models for Agile Processes in the US DoDIEEE Transactions on Software Engineering10.1109/TSE.2021.308066648:8(3117-3130)Online publication date: 1-Aug-2022
https://doi.org/10.1109/TSE.2021.3080666
Rosa WMadachy RClark BBoehm BBener ATurhan BBiffl S(2017)Early phase cost models for agile software processes in the US DoDProceedings of the 11th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement10.1109/ESEM.2017.10(30-37)Online publication date: 9-Nov-2017
https://dl.acm.org/doi/10.1109/ESEM.2017.10
Show More Cited By

Index Terms

Simple empirical software effort estimation model
1. Social and professional topics
  1. Professional topics
    1. Management of computing and information systems
      1. Implementation management
        Pricing and resource allocation

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Published In

cover image ACM Conferences

ESEM '14: Proceedings of the 8th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement

September 2014

461 pages

ISBN:9781450327749

DOI:10.1145/2652524

General Chair:
Maurizio Morisio
Politecnico di Torino, Italy
,
Program Chairs:
Tore Dybå
Sintef, Norway
,
Marco Torchiano
Politecnico di Torino, Italy

Copyright © 2014 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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Publication History

Published: 18 September 2014

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ESEM '14: 2014 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement

September 18 - 19, 2014

Torino, Italy

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ESEM '14 Paper Acceptance Rate 23 of 123 submissions, 19%;

Overall Acceptance Rate 130 of 594 submissions, 22%

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Cited By

Long DDrylie SRitschel JKoschnick C(2024)An Assessment of Rules of Thumb for Software Phase Management, and the Relationship Between Phase Effort and Schedule SuccessIEEE Transactions on Software Engineering10.1109/TSE.2023.333938350:2(209-219)Online publication date: Feb-2024
https://doi.org/10.1109/TSE.2023.3339383
Rosa WClark BMadachy RBoehm B(2022)Empirical Effort and Schedule Estimation Models for Agile Processes in the US DoDIEEE Transactions on Software Engineering10.1109/TSE.2021.308066648:8(3117-3130)Online publication date: 1-Aug-2022
https://doi.org/10.1109/TSE.2021.3080666
Rosa WMadachy RClark BBoehm BBener ATurhan BBiffl S(2017)Early phase cost models for agile software processes in the US DoDProceedings of the 11th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement10.1109/ESEM.2017.10(30-37)Online publication date: 9-Nov-2017
https://dl.acm.org/doi/10.1109/ESEM.2017.10
Huang KKong XSandrasegaran K(2016)Modelling software corrective maintenance productivity using an analytical economic model and simulationInternational Journal of Computer Applications in Technology10.1504/IJCAT.2016.07876754:2(77-88)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1504/IJCAT.2016.078767
Agrawal PKumar S(2016)Early phase software effort estimation model2016 Symposium on Colossal Data Analysis and Networking (CDAN)10.1109/CDAN.2016.7570914(1-8)Online publication date: Mar-2016
https://doi.org/10.1109/CDAN.2016.7570914
Agrawal NJawdekar A(2016)User-based approach for finding various results in web usage mining2016 Symposium on Colossal Data Analysis and Networking (CDAN)10.1109/CDAN.2016.7570867(1-6)Online publication date: Mar-2016
https://doi.org/10.1109/CDAN.2016.7570867

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