A Fuzzy Logic Model for Software Development Effort Estimation at Personal Level

  • Cuauhtemoc Lopez-Martin
  • Cornelio Yáñez-Márquez
  • Agustin Gutierrez-Tornes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4293)


No single software development estimation technique is best for all situations. A careful comparison of the results of several approaches is most likely to produce realistic estimates. On the other hand, unless engineers have the capabilities provided by personal training, they cannot properly support their teams or consistently and reliably produce quality products. In this paper, an investigation aimed to compare a personal Fuzzy Logic System (FLS) with linear regression is presented. The evaluation criteria are based upon ANOVA of MRE and MER, as well as MMRE, MMER and pred(25). One hundred five programs were developed by thirty programmers. From these programs, a FLS is generated for estimating the effort of twenty programs developed by seven programmers. The adequacy checking as well as a validation of the FLS are made. Results show that a FLS can be used as an alternative for estimating the development effort at personal level.


Fuzzy Logic Less Significant Difference Personal Level Fuzzy Logic System Effort Estimation 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ahmed, M.A., Saliu, M.O., AlGhamdi, J.: Adaptive fuzzy logic-based framework for software development effort prediction. Information and Software Technology. Elsevier (2004)Google Scholar
  2. 2.
    Boehm, B., Abts, C., Chulani, S.: Software Development Cost Estimation Approaches – A Survey. Chulani Ph. D. Report (1998)Google Scholar
  3. 3.
    Briand, L.C., Emam, K.E., Surmann, D., Wieczorek, I.: An Assesment and Comparison of Common Software Cost Estimation Modeling Techniques, ISERN-98-27Google Scholar
  4. 4.
    Briand, L.C., Langley, T., Wieczorek, I.: A replicated Assessment and Comparison of Common Software Cost Modeling Techniques. In: IEEE ICSE, Limerick, Ireland (2000)Google Scholar
  5. 5.
    Briand, L.C., Wieczorek, I.: Software Resource Estimation. Encyclopedia of Software Engineering, vol. 2, pp. 1160–1196. John Wiley & Sons, New YorkGoogle Scholar
  6. 6.
    Brooks Jr., F.P.: Three Great Challenges for Half-Century-Old Computer Science. Journal of the ACM 50(1), 25–26 (2003)CrossRefGoogle Scholar
  7. 7.
    Conte, S.D., Dunsmore, H.E., Shen, V.Y.: Software Engineering Metrics and Models. Benjamin/Cummings, M. Park (1986)Google Scholar
  8. 8.
    Höst, M., Wohlin, C.: A subjective effort estimation experiment. IST Journal. Elsevier (1997)Google Scholar
  9. 9.
    Humphrey, W.: A Discipline for Software Engineering. Addison-Wesley, Reading (1995)Google Scholar
  10. 10.
    Humphrey, W.: The Personal Software Process. Technical Report CMU/SEI-2000-022 (2000)Google Scholar
  11. 11.
    Idri, A., Abran, A., Khoshgoftaar, T.: Estimating Software Project Effort by Analogy Based on Linguistic Values. In: Eight IEEE Symposium on Software Metrics (2002)Google Scholar
  12. 12.
    Idri, A., Khoshgoftaar, T.: Fuzzy Analogy: a New Approach for Software Cost Estimation. In: International Workshop on Software Measurement (IWSM 2001), Canada (2001)Google Scholar
  13. 13.
    Kitchenham, B.A., Pfleeger, S.L., Pickard, L.M., Jones, P.W., Hoaglin, D.C., Emam, K.E., Rosenberg, J.: Preliminary Guidelines for Empirical Research in Software Engineering. IEEE Transactions on SE 28(8) (August 2002)Google Scholar
  14. 14.
    Kitchenham, B.A., MacDonell, S.G., Pickard, L.M., Shepperd, M.J.: What Accuracy Statistics Really Measure. IEE Proceedings Software 148(3), 81–85 (2001)CrossRefGoogle Scholar
  15. 15.
    MacDonell, S.G.: Software source code sizing using fuzzy logic modelling. Elsevier Science, Amsterdam (2003)Google Scholar
  16. 16.
    MacDonell, S.G., Gray, A.R.: Alternatives to Regression Models for Estimating Software Projects. In: Proceedings of the IFPUG Fall Conference, Dallas TX, IFPUG (1996)Google Scholar
  17. 17.
    Mendes, E., Mosley, N., Watson, I.: A Comparison of Case-Based Reasoning Approaches to Web Hypermedia project Cost Estimation. ACM Press, New York (2002)Google Scholar
  18. 18.
    Montgomery, D., Peck, E.: Introduction to linear regression analysis. John Wiley, Chichester (2001)MATHGoogle Scholar
  19. 19.
    Park, R.E.: Software Size Measurement: A Framework for Counting Source Statements. Software Engineering Institute, Carnegie Mellon University (September 1992)Google Scholar
  20. 20.
    Schofield, C.: Non-Algorithmic Effort Estimation Techniques. ESERG, TR98-01 (1998)Google Scholar
  21. 21.
    Secretaría de Economía. Programa para el Desarrollo de la Industria del Software (2002)Google Scholar
  22. 22.
    Stensrud, E., Foss, T., Kitchenham, B., Myrtveit, I.: An Empirical Validation of the Relationship Between the Relative Error and Project Size. In: Eighth IEEE SM Symposium (2002)Google Scholar
  23. 23.
    Weiss, N.A.: Introductory Statistics. Addison-Wesley, Reading (1999)MATHGoogle Scholar
  24. 24.
    Zadeh, L.A.: From Computing with Numbers to Computing with Words – From Manipulation of Measurements to Manipulation of Perceptions. IEEE Transactions on Circuits and Systems – I: Fundamental Theory and Applications 45(1), 105–119 (1999)CrossRefMathSciNetGoogle Scholar
  25. 25.
    Zhiwei Xu, Z., Khoshgoftaar, T.M.: Identification of fuzzy models of software cost estimation. Elsevier Fuzzy Sets and Systems 145 (July 2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Cuauhtemoc Lopez-Martin
    • 1
  • Cornelio Yáñez-Márquez
    • 1
  • Agustin Gutierrez-Tornes
    • 2
  1. 1.Center for Computing Research, National Polytechnic Ins0074itute, MexicoMexico D.F.
  2. 2.Systems Coordinator, Banamex; ITESMMexico, D.F.Mexico

Personalised recommendations