Advertisement

Comparing the Estimation Performance of the EPCU Model with the Expert Judgment Estimation Approach Using Data from Industry

  • Francisco Valdés
  • Alain Abran
Part of the Studies in Computational Intelligence book series (SCI, volume 296)

Abstract

Software project estimates are more useful when made early in the project life cycle: this implies that these estimates are to be made in a highly uncertain environment with information that is vague and incomplete.

To tackle these challenges in practice, the estimation method most used at this early stage is the Expert Judgment Estimation approach. However, there are a number of problems with it, such as the fact that the expertise is specific to the people and not to the organization, and the fact that this intuitive estimation expertise is neither well described nor well understood; in addition, the expertise is difficult to assess and cannot be replicated systematically.

Estimation of Projects in Contexts of Uncertainty (EPCU) is an estimation method based on fuzzy logic that mimics the way experts make estimates. This paper describes the experiment designed and carried out to compare the performance of the EPCU model against the Expert Judgment Estimation approach using data from industry projects.

Keywords

EPCU Estimation Projects Uncertainty Contexts Fuzzy Sets Expert Judgment Estimation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bourque, P., Oligny, S., Abran, A., Fournier, B.: Developing Project Duration Models in Software Engineering. Journal of Computer Science and Technology 22, 348–357 (2007)CrossRefGoogle Scholar
  2. 2.
    Project Management Institute, PMBOK Guide – A Guide to the Project Management Body of Knowledge, 3rd edn., p. 378 (2004)Google Scholar
  3. 3.
    Idri, A., Abran, A., Khosgoftaar, T.M.: Fuzzy Analogy: A New Approach for Software Cost Estimation. In: International Workshop on Software Measurement (IWSM 2001), Montréal, Québec (2001)Google Scholar
  4. 4.
    Idri, A., Abran, A., Khoshgoftaar, T.M., Robert, S.: Estimating Software Project Effort by Analogy Based on Linguistic Values. In: 8th IEEE International Software Metrics Symposium, Ottawa, Ontario (2002)Google Scholar
  5. 5.
    Boehm, B.W.: Software Engineering Economics. Prentice Hall, Englewood Cliffs (1985)Google Scholar
  6. 6.
    International Function Point User Group, http://www.ifpug.org/
  7. 7.
    Ribu, Kirsten: Estimating Object-Oriented Software Projects with Use Cases, MSc thesis, University of Oslo, Department of Informatics (November 2001)Google Scholar
  8. 8.
    Myrtveit, E.S.: A Controlled Experiment to Assess the Benefits of Estimating with Analogy and regression Models. IEEE Transaction on Software Engineering 25(4), 510–525 (1999)CrossRefGoogle Scholar
  9. 9.
    Shepperd, M., Schofield, C., Kitchenham, B.: Effort Estimation Using Analogy. In: ICSE-18, Berlin, pp. 170–178 (1996)Google Scholar
  10. 10.
    Idri, A., Abran, A., Khoshgoftaar, T., Robert, S.: Fuzzy Case-Based Reasoning Models for Software Cost Estimation. In: Soft Computing in Software Engineering: Studies in Fuzziness and Soft Computing. Springer, Heidelberg (2004)Google Scholar
  11. 11.
    Kolodner, J.L.: Case-Based Reasoning. Morgan Kaufmann, San Francisco (1993)Google Scholar
  12. 12.
    Kacprzyk, J., Yager, R.R.: Emergency-oriented expert systems: a fuzzy approach. Information Sciences 37(1-3), 143–155 (1985); Referenced in [13]zbMATHCrossRefGoogle Scholar
  13. 13.
    Azadeh, A., et al.: Design and implementation of a fuzzy expert system for perform-ance assessment of an integrated health, safety, environment (HSE) and ergonomics system: The case of a gas refinery. Inform. Sci. (2008), doi:10.1016/j.ins.2008.06.026Google Scholar
  14. 14.
    Idri, A., Abran, A.: Towards A Fuzzy Logic Based Measures for Software Projects Similarity. In: 6th MCSEAI 2000 – Maghrebian Conference on Computer Sciences, Fez, Morocco (2000)Google Scholar
  15. 15.
    Shepperd, M., Schofield, C.: Estimating Software Project Effort Using Analogies. In: ICSE-18, Berlin, pp. 170–178 (1996)Google Scholar
  16. 16.
    Souto, F.V., Abran, A.: Industry Case Studies of Estimation Models Using Fuzzy Sets. In: International Workshop on Software Measurement – IWSM-Mensura 2007, Palma de Mallorca (Spain), November 5-8, pp. 1–15 (2007)Google Scholar
  17. 17.
    Kadoda, G.M., Cartwright, L.C., Shepperd, M.: Experiences Using Case-Based Reasoning to Predict Software Project Effort, EASE, Keele, UK (2000)Google Scholar
  18. 18.
    Jeffery, R., Ruhe, M., Wieczorek, I.: Using public domain metrics to estimate software development effort. In: Seventh International Symposium on Software Metrics 2001, UK, pp. 16–27 (2001)Google Scholar
  19. 19.
    IEEE Standard Glossary of Software Engineering Terminology, IEEE std. 610.12-1990 (1990) Google Scholar
  20. 20.
    Condori-Fernandez, N., Pastor, O., Abran, A., Sellami, A.: Introduciendo Concep-tos de Metrologia en el Diseno de Medidas de Software. In: XI Iberamerico Workshop on Requirements Engineering and Environments, IDEAS 2008, Pernambuco, Brazil, pp. 112–125 (2008)Google Scholar
  21. 21.
    The Standish Group International, Extreme Chaos, The Standish Group International, Inc., Research Reports (2000-2004), http://www.standishgroup.com
  22. 22.
    The Buzz, Off Base: Insufficient expertise in setting baselines hits U.S federal IT budgets where it hurts. PM Network, 21 (March 2007)Google Scholar
  23. 23.
    Zadeh, L.A.: Is there a need for fuzzy logic? Information Sciences 178(13), 2751–2779 (2008)zbMATHCrossRefMathSciNetGoogle Scholar
  24. 24.
    Idri, A., Kjiri, L., Abran, A.: COCOMO Cost Model Using Fuzzy Logic. In: 7th International Conference on Fuzzy Theory &Technology, Atlantic City, New Jersey (2000)Google Scholar
  25. 25.
    IFPUG, Function Point Counting Practices Manual, Version 4.2.1, International Function Points Users Group (2005)Google Scholar
  26. 26.
    Zadeh, L.A.: Is there a need for fuzzy logic? In: Fuzzy Information Processing Society, NAFIPS 2008. Annual Meeting of the North American, May 19-22 (2008)Google Scholar
  27. 27.
    Roychowdhury, S., Wang, B.-H.: Measuring inconsistency in fuzzy rules. In: Fuzzy Systems Proceedings, IEEE World Congress on Computational Intelligence, May 4-9, vol. 2, pp. 1020–1025 (1998)Google Scholar
  28. 28.
    ISO, International Vocabulary of Basic and General Terms in Metrology, International Organization for Standardization, Switzerland, 2nd edn. (1993), ISBN 92-67-01075-1Google Scholar
  29. 29.
    Steve, M.: Software Estimation: Demystifying the Black Art. Microsoft Press (2006)Google Scholar
  30. 30.
    Abran, A.: Estimation and Quality Models Based on Functional Size with COSMIC – ISO 19761. Draft, ch.6 (April 2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Francisco Valdés
    • 1
  • Alain Abran
    • 1
  1. 1.Dept. of Software EngineeringÉcole de Technologie SupérieureMontréalCanada

Personalised recommendations