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Multi-variate Principal Component Analysis of Software Maintenance Effort Drivers

  • Ruchi Shukla
  • A. K. Misra
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6017)

Abstract

The global IT industry has already attained maturity and the number of software systems entering into the maintenance stage is steadily increasing. Further, the industry is also facing a definite shift from traditional environment of legacy softwares to newer softwares. Software maintenance (SM) effort estimation has become one of the most challenging tasks owing to the wide variety of projects and dynamics of the SM environment. Thus the real challenge lies in understanding the role of a large number of SM effort drivers. This work presents a multi-variate analysis of the effect of various drivers on maintenance effort using the Principal Component Analysis (PCA) approach. PCA allows reduction of data into a smaller number of components and its alternate interpretation by analysing the data covariance. The analysis is based on an available real life dataset of 14 drivers influencing the effort of 36 SM projects, as estimated by 6 experts.

Keywords

Software maintenance Effort estimation Principal Component Analysis 

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References

  1. 1.
    IEEE Standard 1219: Standard for Software Maintenance. IEEE Computer Society Press, Los Alamitos (1998)Google Scholar
  2. 2.
    Boehm, B., Abts, C., Chulani, S.: Software development cost estimation approaches – a survey. Ann. Softw. Eng 10, 177–205 (2000)zbMATHCrossRefGoogle Scholar
  3. 3.
    Shukla, R., Misra, A.K.: AI Based Framework for Dynamic Modeling of Software Maintenance Effort Estimation. In: International Conference on Computer and Automation Engineering, pp. 313–317 (2009)Google Scholar
  4. 4.
    Rao, B.S., Sarda, N.L.: Effort drivers in maintenance outsourcing - an experiment using Taguchi’s methodology. In: Seventh IEEE European Conference on Software Maintenance and Reengineering, pp. 1–10 (2003)Google Scholar
  5. 5.
    Ahn, Y., Suh, J., Kim, S., Kim, H.: The software maintenance project effort estimation model based on function points. J. Softw. Maint. Evol.: Res. and Pract. 15(2), 71–78 (2003)CrossRefGoogle Scholar
  6. 6.
    Bhatt, P., Shroff, G., Anantram, C., Misra, A.K.: An influence model for factors in outsourced software maintenance. J. Softw. Maint. Evol.: Res. and Pract. 18, 385–423 (2006)CrossRefGoogle Scholar
  7. 7.
    Bhatt, P., Williams, K., Shroff, G., Misra, A.K.: Influencing factors in outsourced software maintenance. ACM SIGSOFT Softw. Eng. Notes 31(3), 1–6 (2006)CrossRefGoogle Scholar
  8. 8.
    Jorgensen, M.: Experience with accuracy of software maintenance task effort prediction models. IEEE Trans. Softw. Eng., 674–681 (1995)Google Scholar
  9. 9.
    Martín, C.L., Márquez, C.Y., Tornés, A.G.: Predictive accuracy comparison of fuzzy models for software development effort of small programs. J. Syst. Softw. 81(6), 949–960 (2008)CrossRefGoogle Scholar
  10. 10.
    Srinivasan, K., Fisher, D.: Machine learning approaches to estimating software development effort. IEEE Trans. Softw. Eng. 21(2), 126–137 (1995)CrossRefGoogle Scholar
  11. 11.
    Grimstad, S., Jørgensen, M.: Inconsistency of expert judgment-based estimates of software development effort. J. Syst. Softw. 80(11), 1770–1777 (2007)CrossRefGoogle Scholar
  12. 12.
    Shukla, R., Misra, A.K.: Estimating Software Maintenance Effort - A Neural Network Approach. In: 1st India Software Engineering Conference, Hyderabad, pp. 107–112. ACM Digital Library (2008)Google Scholar
  13. 13.
    Pendharkar, P.C., Subramanian, G.H., Rodger, J.A.: A probabilistic model for predicting software development effort. IEEE Trans. Softw. Eng. 31(7), 615–624 (2005)CrossRefGoogle Scholar
  14. 14.
    Shukla, K.K.: Neuro-genetic prediction of software development effort. Inform. Softw. Tech. 42, 701–713 (2000)CrossRefGoogle Scholar
  15. 15.
    Phadke, M.S.: Quality Engineering Using Robust Design. Prentice-Hall, Englewood cliffs (1989)Google Scholar
  16. 16.
    Fung, C.P., Kang, P.C.: Multi-response optimization in friction properties of PBT composites using Taguchi method and principal component analyses. J. Mater. Proc. Tech. 170, 602–610 (2005)CrossRefGoogle Scholar
  17. 17.
  18. 18.
    Shukla, R., Misra, A.K.: Software Maintenance Effort Estimation - Neural Network Vs Regression Modeling Approach. Int. J. Futur. Comp. Applic. (Accepted, 2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ruchi Shukla
    • 1
  • A. K. Misra
    • 1
  1. 1.Computer Science and Engineering DepartmentMotilal Nehru National Institute of TechnologyAllahabadIndia

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