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research-article

Software Productivity Measurement Using Multiple Size Measures

Authors:

Barbara Kitchenham,

Emilia MendesAuthors Info & Claims

IEEE Transactions on Software Engineering, Volume 30, Issue 12

Pages 1023 - 1035

https://doi.org/10.1109/TSE.2004.104

Published: 01 December 2004 Publication History

Abstract

Productivity measures based on a simple ratio of product size to project effort assume that size can be determined as a single measure. If there are many possible size measures in a data set and no obvious model for aggregating the measures into a single measure, we propose using the expression AdjustedSize/Effort to measure productivity. AdjustedSize is defined as the most appropriate regression-based effort estimation model, where all the size measures selected for inclusion in the estimation model have a regression parameter significantly different from zero (p < 0.05). This productivity measurement method ensures that each project has an expected productivity value of one. Values between zero and one indicate lower than expected productivity, values greater than one indicate higher than expected productivity. We discuss the assumptions underlying this productivity measurement method and present an example of its use for Web application projects. We also explain the relationship between effort prediction models and productivity models.

References

[1]

A. Albrecht and J. Gaffney, “Software Function, Source Lines of Code and Development Effort Prediction: A Software Science Validation,” IEEE Trans. Software Eng., vol. 9, no. 6, 1983.

Digital Library

[2]

M. Arnold and P. Pedross, “Software Size Measurement and Productivity Rating In a Large-Scale Software Development Department,” Proc. 20th Int'l Conf. Software Eng., pp. 490-493, Apr. 1998.

Digital Library

[3]

B.W. Boehm, Software Engineering Economics. Prentice-Hall, 1981.

Digital Library

[4]

S.R. Chidamber D.P. Darcy and C.F. Kemerer, “Managerial Use of Metrics for Object-Oriented Software: An Exploratory Analysis,” IEEE Trans. Software Eng., vol. 24, no. 8, pp. 629-639, Aug. 1998.

Digital Library

[5]

S. Elbaum D. Gable and G. Rothermel, “Understanding and Measuring the Sources of Variation in the Prioritization of Regression Test Suites,” Proc. Seventh Int'l Software Metrics Symp., pp. 169-179, Apr. 2001.

Digital Library

[6]

M.H. Halstead, Elements of Software Science. Elsevier, 1977.

Digital Library

[7]

T.E. Hastings and A.S.M. Sajeev, “A Vector-Based Approach to Software Size Measurement and Effort Estimation,” IEEE Trans. Software Eng., vol. 27, no. 4, pp. 337-350, Apr. 2001.

Digital Library

[8]

T.E. Hastings and A.S.M. Sajeev, “A Vector-Based Software Size Measurement,” Proc. Australian Software Eng. Conf. (ASWEC '97), pp. 7-15, 1997.

Digital Library

[9]

B.A. Kitchenham, “Procedures for Performing Systematic Reviews,” Joint Technical Report Keele University TR/SE0104 and NICTA 0400011T.1, pp. 1-28, 2004.

[10]

B.A. Kitchenham, “The Question of Scale Economies In Software-Why Cannot Researchers Agree?” Information and Software Technology, vol. 44, pp. 13-24, 2002.

Digital Library

[11]

B.A. Kitchenham, “A Procedure for Analysing Unbalanced Datasets,” IEEE Trans. Software Eng., vol. 24, no. 4, pp. 278-301, Apr. 1998.

Digital Library

[12]

B.A. Kitchenham L.M. Pickard S.G. Macdonell and M.J. Shepperd, “What Accuracy Statistics Really Measure,” IEE Proc. -Software, vol. 148, no. 3, pp. 81-85, 2001.

[13]

B.A. Kitchenham and E. Mendes, “A Comparison of Cross-Company and Within-Company Effort Estimation Models for Web Applications,” Proc. Empirical Assessment in Software Eng., pp.nbsp47-56, May 2004.

[14]

B.A. Kitchenham and E. Mendes, “Systematic Review of Software Productivity Measurement,” http://www.cs.auckland.ac.nz/emilia/srspp.pdf, 2004.

[15]

B.A. Kitchenham T. Dybå and M. Jørgensen, “Evidence-Based Software Engineering,” Proc. Int'l Conf. Software Eng., pp. 273-281, May 2004.

Digital Library

[16]

A. MacCormack C. Kemerer M. Cusumano and B. Crandall, “Trade-Offs between Productivity and Quality in Selecting Software Development Practices,” IEEE Software, pp. 78-79, Sept./Oct. 2003.

Digital Library

[17]

K.D. Maxwell, Applied Statistics for Software Managers. Software Quality Institute Series, Prentice-Hall, 2002.

[18]

K.D. Maxwell and P. Forselius, “Benchmarking Software Development Productivity,” IEEE Software, vol. 17, no. 1, pp. 80-88, Jan./Feb. 2000.

Digital Library

[19]

E. Mendes N. Mosley and S. Counsell, “Investigating Early Web Size Measures for Web Cost Estimation,” Proc. Int'l Conf. Empirical Assessment in Software Eng., pp. 1-22, 2003.

[20]

S. Morasca and G. Russo, “An Empirical Study of Software Productivity,” Proc. 25th Ann. Int'l Computer Software and Applications Conf., pp. 317-322, Oct. 2001.

Digital Library

[21]

S. Moser and O. Nierstrasz, “The Effect of Object-Oriented Frameworks on Developer Productivity,” Computer, vol. 29, no. 9, pp. 45-51, Sept. 1996.

Digital Library

[22]

I. Myrtveit and E. Stensrud, “Benchmarking COTS Projects Using Data Envelopment Analysis,” Proc. Sixth Int'l Software Metrics Symp., pp. 269-278, Nov. 1999.

Digital Library

[23]

D. Reifer, “Web-Development: Estimating Quick-Time-to-Market Software,” IEEE Software, vol. 17, no. 8, pp. 57-64, Nov./Dec. 2000.

Digital Library

[24]

J. Rosenberg, “Some Misconceptions about Lines of Code,” Proc. Fourth Int'l Software Metrics Symp., pp. 137-142, Nov. 1997.

Digital Library

[25]

W.R. Shadish T.D. Cook and D.T. Campbell, Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin Company, 2002.

[26]

E. Stensrud and I. Myrtveit, “Identifying High Performance ERP Projects,” IEEE Trans. Software Eng., vol. 29, no. 5, pp. 398-416, May 2003.

Digital Library

[27]

S.C. Kumbhakar and C.A.K. Lovell, Stochastic Frontier Analysis. Cambridge Univ. Press, 2000.

Digital Library

Cited By

Sas DAvgeriou P(2023)An Architectural Technical Debt Index Based on Machine Learning and Architectural SmellsIEEE Transactions on Software Engineering10.1109/TSE.2023.328617949:8(4169-4195)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1109/TSE.2023.3286179
Reinhartz-Berger I(2023)Challenges in software model reuse: cross application domain vs. cross modeling paradigmEmpirical Software Engineering10.1007/s10664-023-10386-929:1Online publication date: 5-Dec-2023
https://dl.acm.org/doi/10.1007/s10664-023-10386-9
Azzeh MNassif AElsheikh YAngelis L(2022)On the value of project productivity for early effort estimationScience of Computer Programming10.1016/j.scico.2022.102819219:COnline publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1016/j.scico.2022.102819
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Index Terms

Software Productivity Measurement Using Multiple Size Measures
1. 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
2. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Process validation

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cover image IEEE Transactions on Software Engineering

IEEE Transactions on Software Engineering Volume 30, Issue 12

December 2004

256 pages

ISSN:0098-5589

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Copyright © 2004.

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IEEE Press

Publication History

Published: 01 December 2004

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Cited By

Sas DAvgeriou P(2023)An Architectural Technical Debt Index Based on Machine Learning and Architectural SmellsIEEE Transactions on Software Engineering10.1109/TSE.2023.328617949:8(4169-4195)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1109/TSE.2023.3286179
Reinhartz-Berger I(2023)Challenges in software model reuse: cross application domain vs. cross modeling paradigmEmpirical Software Engineering10.1007/s10664-023-10386-929:1Online publication date: 5-Dec-2023
https://dl.acm.org/doi/10.1007/s10664-023-10386-9
Azzeh MNassif AElsheikh YAngelis L(2022)On the value of project productivity for early effort estimationScience of Computer Programming10.1016/j.scico.2022.102819219:COnline publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1016/j.scico.2022.102819
Lourenço HFerreira CCosta Seco JParreira J(2022)OSTRICH: a rich template language for low-code development (extended version)Software and Systems Modeling (SoSyM)10.1007/s10270-022-01066-122:5(1645-1663)Online publication date: 16-Dec-2022
https://dl.acm.org/doi/10.1007/s10270-022-01066-1
Azzeh MNassif AMartín C(2021)Empirical analysis on productivity prediction and locality for use case points methodSoftware Quality Journal10.1007/s11219-021-09547-029:2(309-336)Online publication date: 1-Jun-2021
https://dl.acm.org/doi/10.1007/s11219-021-09547-0
Goyal MGupta C(2020)Intuitionistic Fuzzy Decision Making Towards Efficient Team Selection in Global Software DevelopmentJournal of Information Technology Research10.4018/JITR.202004010513:2(75-93)Online publication date: 1-Apr-2020
https://dl.acm.org/doi/10.4018/JITR.2020040105
Mahmood YKama NAzmi A(2020)A systematic review of studies on use case points and expert‐based estimation of software development effortJournal of Software: Evolution and Process10.1002/smr.224532:7Online publication date: 2-Jul-2020
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Canedo ESantos GMachado ISouza RMaciel RSant'Anna C(2019)Factors Affecting Software Development ProductivityProceedings of the XXXIII Brazilian Symposium on Software Engineering10.1145/3350768.3352491(307-316)Online publication date: 23-Sep-2019
https://dl.acm.org/doi/10.1145/3350768.3352491
Duarte CSutton SArmbrust OHebig R(2019)The quest for productivity in software engineeringProceedings of the International Conference on Software and System Processes10.1109/ICSSP.2019.00027(145-154)Online publication date: 25-May-2019
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Elias GJain R(2017)Assessing systems architectureInternational Journal of Business Information Systems10.1504/IJBIS.2017.08144624:2(127-173)Online publication date: 1-Jan-2017
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