Effort Estimation Using Social Choice

  • Stefan Koch
  • Johann Mitloehner


In this chapter, we argue for adopting mechanisms from the field of social choice for effort estimation. Social choice deals with aggregating the preferences of a number of individuals into a single ranking. We will use this idea, substituting these voters by different project attributes. Therefore a new project only needs to be placed into rankings per attribute, necessitating only ordinal values. Using the resulting aggregate ranking, the new project is again placed between other projects, whose actual expended effort can be used to derive an estimation. In this chapter, we will present this method together with a sample application and validation based on the well-known COCOMO 81 data set.


Social Choice Vote Rule Project Attribute Aggregation Rule Condorcet Winner 
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.


  1. Albrecht, A.J. and J.E. Gaffney (1983) “Software Function, Source Lines of Code, and Development Effort Prediction: A Software Science Validation”, IEEE Trans Software Eng, 9(6), 639–648.CrossRefGoogle Scholar
  2. Boehm, B.W. (1981) Software Engineering Economics, Prentice Hall, Englewood Cliffs, NJ.Google Scholar
  3. Boehm, B.W., Abts, C., and S. Chulani (2000a) “Software development cost estimation approaches — A survey”, Annals of Software Engineering, 10, 177–205.MATHCrossRefGoogle Scholar
  4. Boehm, B.W., Abts, C., Brown, A.W., Chulani, S., Clark, B.K., Horowitz, E., Madachy, R., Reifer, D.J., and B. Steece (2000b) Software Cost Estimation with COCOMO II,Prentice Hall PTR, Upper Saddle River, NJ.Google Scholar
  5. Eckert, D., Klamler, C., Mitlöhner, J., and C. Schlötterer (2006) “A Distance-based Comparison of Basic Voting Rules”, Central European Journal of Operations Research, 14(4), 377–386.MathSciNetMATHCrossRefGoogle Scholar
  6. Fishburn, P. C. (1977) “Condorcet Social Choice Functions”, SIAM Journal of Applied Mathematics, 33, 469–489.MathSciNetMATHCrossRefGoogle Scholar
  7. Jorgensen, M. and M. Shepperd (2007) “A Systematic Review of Software Development Cost Estimation Studies”, IEEE Trans Software Eng, 33(1), 33–53.CrossRefGoogle Scholar
  8. Kitchenham, B. and E. Mendes (2004) “Software Productivity Measurement Using Multiple Size Measures”, IEEE Trans Software Eng, 30(12), 1023–1035.CrossRefGoogle Scholar
  9. Klamler, C. (2005) “On the Closeness Aspect of Three Voting Rules: Borda — Copeland — Maximin”, Group Decision and Negotiation, 14(3), 233–240.MathSciNetCrossRefGoogle Scholar
  10. MacDonnel, S.G., and A.R. Gray (1996) “Alternatives to Regression models for estimating Software Projects”, Proc IFPUG Fall Conference, Dallas, Texas, pp. 279.1–279.15.Google Scholar
  11. Matson, J.E., Barrett, B.E., and J.M. Mellichamp (1994) “Software Development Cost Estimation Using Function Points”, IEEE Trans Software Engineering, 20(4), 275–287.CrossRefGoogle Scholar
  12. Miranda, E. (2001) “Improving Subjective Estimates Using Paired Comparisons”, IEEE Software, 18(1), 87–91.MathSciNetCrossRefGoogle Scholar
  13. Myrtveit, I., and E. Stensrud (1999) “A Controlled Experiment to Assess the Benefits of Estimating with Analogy and Regression Models”, IEEE Trans Software Eng, 25(4), 510–525.CrossRefGoogle Scholar
  14. Putnam, L.H. (1978) “A General Empirical Solution to the Macro Software Sizing and Estimating Problem”, IEEE Trans Software Eng, 4(4), 345–361.CrossRefGoogle Scholar
  15. Saari, D. (2001) Decisions and Elections — Explaining the Unexpected, Cambridge University Press.Google Scholar
  16. Saaty T.L. (1990) Multicriteria Decision Making: The Analytic Hierarchy Process, RWS Publications, Pittsburgh.Google Scholar
  17. Selby, R.W., and A.A. Porter (1988) “Learning from Examples: Generation and Evaluation of Decision Trees for Software Resource Analysis”, IEEE Trans Software Eng, 14(12), 1743–1756.CrossRefGoogle Scholar
  18. Shepperd, M., and C. Schofield (1997) “Estimating Software Project Effort Using Analogies”, I IEEE Trans Software Eng, 23(12), 736–743.CrossRefGoogle Scholar
  19. Shepperd, M., Schofield, C., and B. Kitchenham (1996) “Effort Estimation Using Analogy”, Proc ICSE 1996, Berlin, Germany, pp. 170–178.Google Scholar
  20. Srinivasan, K., and D. Fisher (1995) “Machine Learning Approaches to Estimating Software Development Effort”, IEEE Trans Software Eng, 21(2), 126–137.CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Stefan Koch
  • Johann Mitloehner

There are no affiliations available

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