Advertisement

Evaluation of Project Quality: A DEA-Based Approach

  • Shen Zhang
  • Yongji Wang
  • Jie Tong
  • Jinhui Zhou
  • Li Ruan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3966)

Abstract

The evaluation of project quality exhibits multivariable, VRS (variable return to scale) and decision maker’s preference properties. In this paper, we present a Data Envelopment Analysis (DEA) based evaluation approach. The DEA VRS model, which handles multivariable and VRSeffectively, is used to measure project quality. And the DEA cone ratio model, which utilizes Analytical Hierarchy Process (AHP) to constrain quality metrics with respect to decision maker’s preference, is also adopted to analyze the return to scaleof the projects. A case study, which assesses 10 projects from ITECHS and 20 “Top active” projects on sourceforge.net with the novel method, is demonstrated. The results indicate that our approach is effective for quality evaluation and can get accurate estimates of future possible improvements.

Keywords

Data Envelopment Analysis Analytical Hierarchy Process Data Envelopment Analysis Model Quality Metrics Decision Make Unit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Stensrud, E., Myrtveit, I.: Identifying High Performance ERP Projects. IEEE Transaction on Software Engineering 29(5), 387–416 (2003)CrossRefGoogle Scholar
  2. 2.
    Paradi, J.C., Reese, D.N., Rosen, D.: Applications of DEA to measure the efficiency of software production at two large Canadian banks. Annals of Operations Research 73, 91–115 (1997)CrossRefzbMATHGoogle Scholar
  3. 3.
  4. 4.
  5. 5.
    Malaiya, Y.K., Denton, J.: Module Size Distribution and Defect Density Software Reliability Engineering. Software Reliability Engineering, 62–71 (2000)Google Scholar
  6. 6.
    Rosenberg, J.: Some Misconceptions About Lines of Code. In: Software Metrics Symposium, pp. 137–142 (1997)Google Scholar
  7. 7.
    Golden, B.L., Wasil, E.A., Harker, P.T. (eds.): The Analytic Hierarchy Process - Applications and Studies. Springer, Heidelberg (1989)Google Scholar
  8. 8.
    Liang, L., Cui, J.C.: Selection of Input-output Items and Data Disposal in DEA. Journal of Systems Engineering 18(6), 487–490 (2003)Google Scholar
  9. 9.
    Banker, R.D., Charnes, A., Cooper, W.W.: Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science 30(9), 1078–1092 (1984)CrossRefzbMATHGoogle Scholar
  10. 10.
    Dinh-Trong, T., Bieman, J.M.: Open source software development: a case study of FreeBSD Software Metrics. Software Metrics, 96–105 (2004)Google Scholar
  11. 11.
    Charnes, A., Cooper, W.W., Wei, Q.L., et al.: Cone Ratio Data Envelopment Analysis and Multi-Objective Programming. International Journal of System Science 20(7), 1099–1118 (1989)CrossRefzbMATHGoogle Scholar
  12. 12.
    Charnes, A., Cooper, W.W., Rhodes, E.: Measuring the Efficiency of Decision Making Units. European J. Operational Research 2, 429–444 (1978)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Shen Zhang
    • 1
    • 2
  • Yongji Wang
    • 1
    • 3
  • Jie Tong
    • 1
    • 3
  • Jinhui Zhou
    • 1
    • 3
  • Li Ruan
    • 1
    • 3
  1. 1.Laboratory for Internet Software Technologies, Institute of SoftwareThe Chinese Academy of SciencesBeijingChina
  2. 2.Key Laboratory for Computer ScienceThe Chinese Academy of SciencesBeijingChina
  3. 3.The Chinese Academy of SciencesGraduate UniversityBeijingChina

Personalised recommendations