A Novel Model for Software Effort Estimation Using Exponential Regression as Firing Interval in Fuzzy Logic

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 142)


Software effort estimation is the process of estimating the cost and time required to develop a software system. It plays a prominent role in software project decisions like resource allocation and bidding which are major parts of planning where as the substratal goals of planning are to scout for the future, to diagnose the attributes that are essentially done for the consummation of the project successfully. So, the effective Software cost estimation is one of the most challenging and important activities in Software development. This paper articulates the new model using fuzzy logic to estimate effort required in software development. We use MATLAB for tuning the parameters of famous various cost estimation models. The performance of model is evaluated on published software projects data. Comparison of results from our model with existing ubiquitous models is done.


Fuzzy Logic Effort Estimation KLOC COCOMO Fuzziness Membership Function 


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  1. 1.
    Mittal, A., Parkash, K., Mittal, H.: Software Cost Estimation using fuzzy logic. ACM SIGSOFT Software Engineering Notes 35(1) (November 2010)Google Scholar
  2. 2.
    Zmud, R.W., Kemerer, C.F.: An Empirical Validation of Software Cost Estimation Models. Communication of the ACM 30(5) (May 1987)Google Scholar
  3. 3.
    Zadeh, L.A.: Fuzzy sets. Info and Control 8, 338–353 (1965)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Galindo, J.: Handbook of Research in Fuzzy Information Processing in Databases. In: Information Science Reference (2008)Google Scholar
  5. 5.
    Johnson, K.: Software Cost Estimation: Metrics and Models, pp. 1–17. Dept of Computer Science, University of Calgary, Alberta, CanadaGoogle Scholar
  6. 6.
    Hari, C.V.M.K., et al.: Identifying the Importance of Software Reuse in COCOMO81, COCOMOII. International Journal on Computer Science and Engineering 1(3), 142–147 (2009); ISSN: 0975-3397Google Scholar
  7. 7.
    Baiely, Basili, j.w.: A Metamedel for Software Development Resource Expenditure. In: Proc. Intl. Conference Software Egg., pp. 107–115 (1981)Google Scholar
  8. 8.
    Boehm, B.: Software Engineering Economics. Prentice Hall, Englewood Cliffs (1981)zbMATHGoogle Scholar
  9. 9.
    Boehm, B.: Cost Models for Future Life Cycle Process: COCOMO2. Annals of Software Engineering (1995)Google Scholar
  10. 10.
    jalote, P.: An Integrated Approach for Software Engineering, 3rd edn., ISBN: 978-81-7319-702-4Google Scholar
  11. 11.
    Jorgensen, M., Grimstad, S.: Over- Optimism in Software Development Projects: The Winner’s Curse. In: CONIELECOMP 2005. Simula Research Laboratory, Norway (2005)Google Scholar
  12. 12.
    Menzies, T., Port, D., Chen, Z., Hihn, J., Stukes, S.: Validation Methods for calibrating software effort models. In: ICSE 2005: Proceedings of the 27th International Conference on Software Engineering, pp. 587–595. ACM Press, New York (2005)Google Scholar
  13. 13.
    Lotfi Zadeh, A.: Fuzzy Logic, Neural Networks and Soft Computing. Communication of ACM 37(3), 77–84 (1994)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.CSE Dept.Gudlavalleru Engineering CollegeGudlavalleruIndia
  2. 2.BS&H Dept.Gudlavalleru Engineering CollegeGudlavalleruIndia
  3. 3.School of IT and Engg.VIT UniversityVelloreIndia
  4. 4.IT DeptGudlavalleru Engineering CollegeGudlavalleruIndia

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