A New Regression Based Software Cost Estimation Model Using Power Values

  • Oktay Adalier
  • Aybars Uğur
  • Serdar Korukoğlu
  • Kadir Ertaş
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4881)


The paper aims to provide for the improvement of software estimation research through a new regression model. The study design of the paper is organized as follows. Evaluation of estimation methods based on historical data sets requires that these data sets be representative for current or future projects. For that reason the data set for software cost estimation model the International Software Benchmarking Standards Group (ISBSG) data set Release 9 is used. The data set records true project values in the real world, and can be used to extract information to predict new projects cost in terms of effort. As estimation method regression models are used. The main contribution of this study is the new cost production function that is used to obtain software cost estimation. The new proposed cost estimation function performance is compared with related work in the literature. In the study same calibration on the production function is made in order to obtain maximum performance. There is some important discussion on how the results can be improved and how they can be applied to other estimation models and datasets.


Software Cost Estimation Regression Analysis Software Cost Models 


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  1. 1.
    Grimstad, S., Jorgensen, M., Østvold, K.M.: Software Effort Estimation Terminology: The tower of Babel. Information and Software Technology 48, 302–310 (2006)CrossRefGoogle Scholar
  2. 2.
    Boraso, M., Montangero, C., Sedehi, H.: Software Cost Estimation: an experimental study of model performances, Technical Report: TR-96-22, University of Pisa, ItalyGoogle Scholar
  3. 3.
    Wieczorek, I., Ruhe, M.: How Valuable is company-specific Data Compared to multi-company Data for Software Cost Estimation? In: METRICS 2002. Proceedings of the Eighth IEEE Symposium on Software Metrics (2002)Google Scholar
  4. 4.
    Jones, C.: Applied Software Measurement: Assuring Productivity and Quality. McGraw-Hill, New York (1991)zbMATHGoogle Scholar
  5. 5.
    Bontempi, G., Kruijtzer, K.: The use of intelligent data analysis techniques for system-level design: a software estimation example. Soft Computing 8, 477–490 (2004)zbMATHCrossRefGoogle Scholar
  6. 6.
    Jorgensen, M., Shepperd, M.: A Systematic Review of Software Development Cost Estimation Studies. IEEE Transactions On Software Engineering 33(1) (January 2007)Google Scholar
  7. 7.
    Liu, Q., Mintram, R.C.: Preliminary Data Analysis Methods in Software Estimation. Software Quality Journal 13, 91–115 (2005)CrossRefGoogle Scholar
  8. 8.
    Stensrud, E., Myrtveit, I.: Human Performance Estimating with Analogy and Regression Models: An Empirical Validation. In: METRICS 1998. Fifth International Symposium on Software Metrics (1998)Google Scholar
  9. 9.
    Hu, Q., Plant, R.T., Hertz, D.B.: Software Cost Estimation Using Economic Production Models. Journal of Management Information System 15(1), 143–163 (1998)Google Scholar
  10. 10.
    Dolado, J.J.: On the problem of the software cost function. Information and Software Technology 43, 61–72 (2001)CrossRefGoogle Scholar
  11. 11.
    ISBSG: International Software Benchmarking Standards Group,
  12. 12.
    Finnie, G.R., Wittig, G.E., Desharnais, J.M.: A Comparison of Software Effort Estimation Techniques: Using Function Points with Neural Networks, Case-Based Reasoning and Regression Models. Journal of Systems Software 39, 281–289 (1997)CrossRefGoogle Scholar
  13. 13.
    Delany, S.J., Cunningham, P., Wilke, N.: The limits of CBR in Software Project Estimation. In: German Workshop on Case-Based Reasoning (1998)Google Scholar
  14. 14.
    Shepperd, M., Schofield, C.: Estimating Software Project Effort Using Anologies. IEEE Transactions on Software Engineering 23(12) (November 1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Oktay Adalier
    • 1
  • Aybars Uğur
    • 2
  • Serdar Korukoğlu
    • 2
  • Kadir Ertaş
    • 3
  1. 1.TUBITAK-UEKAE, National Research Institute of Electronics and Cryptology, PK 74 Gebze KOCAELITurkey
  2. 2.Ege University, Department of Computer Engineering, Bornova IZMIRTurkey
  3. 3.Dokuz Eylül University, Department of Econometrics, Buca, IZMIRTurkey

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