Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Springer Nature Link
Log in

Estimating Maintenance Effort by Analogy

  • Published:
Empirical Software Engineering Aims and scope Submit manuscript

Abstract

Effort estimation is a key step of any software project. This paper presents a method to estimate project effort using an improved version of analogy. Unlike estimation methods based on case-based reasoning, our method makes use of two nearest neighbors of the target project for estimation. An additional refinement based on the relative location of the target project is then applied to generate the effort estimate. We first identify the relationships between cost drivers and project effort, and then determine the number of past project data that should be used in the estimation to provide the best result. Our method is then applied to a set of maintenance projects. Based on a comparison of the estimation results from our estimation method and those of other estimation methods, we conclude that our method can provide more accurate results.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Aron, J. D. 1969. Estimating Resource for Large Programming Systems. Rome, Italy: NATO Science Committee.

    Google Scholar 

  • Banker, R. H., Chang, H., and Kemerer, C. 1994. Evidence on economies of scale in software development. Information andSoftware Technology 36(5): 275–282.

    Google Scholar 

  • Black, R. K. D., Curnow, R. P., Katz, R., and Gray, M. D. 1977. BCS Software Production Data, Final Technical Report, RADC-TR-77-116, Boeing Computer Services, Inc.

  • Boehm, B. 1981. Software Engineering Economics. Englewood Cliff, NJ: Prentice-Hall.

    Google Scholar 

  • Boehm, B., Clark, B., Horowitz, E., and Westland, C. 1995. Cost models for future software life cycle processes: COCOMO 2.0. Annals of Software Engineering 1: 57–94.

    Google Scholar 

  • Briand, L., Langley, T., and Wieczorek, I. 2000. A replicated assessment and comparison of common software cost modeling techniques. In: Proceedings of the International Conference on Software Engineering, 377–386.

  • Conte, E., Dunsmore, H. E., and Shen, V. Y. 1986. Software Engineering Metrics andModels. Menlo Park: Benjamin Cummings.

    Google Scholar 

  • Donelson, W. S. 1976. Project planning and control, Datamation, (June): 73–80.

  • Gaffney, Jr. J. E., and Werling, R. 1993. Estimating software size from counts of externals, a generalization of function points. In: eds Gulledge, T. R. and Hutzler, W. P. Analytical Methods in Software Engineering Economics, Berlin: Springer-Verlag.

    Google Scholar 

  • Gray, A. R., and MacDonell S. G. 1997. A comparison of techniques for developing predictive models of software metrics. Information andSoftware Technology 39(6): 425–437.

    Google Scholar 

  • Kemerer, C. F., 1987. An empirical validation of software cost estimation models. Communication of the ACM 30(5): 436–445.

    Google Scholar 

  • Mukhopadhyay, T., Vicinanza, S. S., and Prietula, M. J. 1992. Examining the feasibility of a case based reasoning model for software effort estimation, MIS Quarterly 16: 156–173.

    Google Scholar 

  • Parkinson, G. N. 1957. Parkinson's Law and Other Studies in Administration. Boston: Houghton-Miffin.

    Google Scholar 

  • Putnam, L.H. 1978. A general empirical solution to the macro software sizing and estimating problem. IEEE Transactions on Software Engineering SE-4(4) (July): 345–361.

    Google Scholar 

  • Shepperd, M., and Schofield, C. 1997. Estimating software project effort using analogies, IEEE Transactions on Software Engineering 23(12): 736–743.

    Google Scholar 

  • Srinivasan, K., and Fisher, D. 1995. Machine learning approaches to estimating software development effort. IEEE Transactions on Software Engineering., 21: 126–137.

    Google Scholar 

  • Thayer, R. H., and McGettrick, A. D. 1993. Software Engineering, A European Perspective. Los Alamitos, CA: IEEE Computer Society Press.

    Google Scholar 

  • Walston, C. E., and Felix, C. P. 1977. A method of programming measurement and estimation, IBM Systems Journal 16(1): 54–73.

    Google Scholar 

  • Wittig, G. E., and Finnie, G. R. 1994, Using artificial neural networks and function points to estimate 4GL software development effort. Australian Journal of Information Systems 1(2) 341–358.

    Google Scholar 

  • Wolverton, R. W. 1974. The cost of developing large-scale software, IEEE Transactions on Computer c-23(6): 615–636.

    Google Scholar 

  • Wong, C. M. 1999. Dimensional analysis for estimating software project effort using analogies. MSc in Information Technology Dissertation, Hong Kong Polytechnic University.

Download references

Author information

Authors and Affiliations

  1. Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

    Hareton K. N. Leung

Authors
  1. Hareton K. N. Leung
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Leung, H.K.N. Estimating Maintenance Effort by Analogy. Empirical Software Engineering 7, 157–175 (2002). https://doi.org/10.1023/A:1015202115651

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1015202115651

Search

Navigation