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A Productivity Optimising Model for Improving Software Effort Estimation

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1294)

Abstract

The estimation of software development effort is a critical task for the effective management of any software industry. Despite the fact that it has been under development for a long time - along with many contributions from many authors seeking to improve the accuracy of software effort estimation, it is still of great interest to many researchers. This study proposed an improved effort estimation model, named the Productivity Optimising Model. This model was designed, based on the Function Points Measurement method and the Multiple Linear Regression model. The Multiple Linear Regression model was built based on the research of historical datasets in order to provide an estimation model so that one can determine the optimising productivity, and then it is easy to calculate the effort. The effort result of this model was compared to the others that were calculated by the Mean Value of Productivity of the tested dataset, and the Capers Jones method. It proved that proposed method gives better accuracy results than the other models.

Keywords

Functional Size Measurement (FSM) Effort estimation Effort accuracy 

Notes

Acknowledgment

This work was supported by the Faculty of Applied Informatics, Tomas Bata University in Zlín, under Project SV13202001020-PU30, Project IGA/CebiaTech/2020/001, and Project RVO/FAI/2020/002.

References

  1. 1.
    IFPUG: International Function Point Users Group. http://www.ifpug.org/
  2. 2.
    Boehm, B.: Software Engineering Economics. Prentice-Hall, Englewood Cliffs (1981). ISBN 0-13-822122-7zbMATHGoogle Scholar
  3. 3.
    Albrecht, A.J.: Measuring application development productivity. In: Proceedings of the IBM Applications Development Symposium, p. 83 (1979)Google Scholar
  4. 4.
    Symons, C.: Function point analysis - difficulties and improvements. IEEE Trans. Softw. Eng. 14(1), 2–11 (1988)CrossRefGoogle Scholar
  5. 5.
    ISO/IEC 20926:2009: (IFPUG) Software and systems engineering. Software measurement. IFPUG functional size measurement method (2009)Google Scholar
  6. 6.
    International Function Point User Group (IFPUG): Function point counting practices manual-release 4.3.1 (2010)Google Scholar
  7. 7.
    Albrecht, A.J., Gaffney, J.E.: Software function, source lines of codes and development effort prediction: a software science validation. IEEE Trans. Softw. Eng. SE-9, 639–648 (1983)CrossRefGoogle Scholar
  8. 8.
    ISBSG, ISBSG release 2018 R2Google Scholar
  9. 9.
    Mendenhall, W.: A Second Course in Statistics: Regression Analysis. Pearson Education Inc., Boston (2012)Google Scholar
  10. 10.
    Khatibi Bardsiri, V., Jawawi, D.N.A., Hashim, S.Z.M., Khatibi, E.: A flexible method to estimate the software development effort based on the classification of projects and localization of comparisons. Empir. Softw. Eng. 19(4), 857–884 (2013)CrossRefGoogle Scholar
  11. 11.
    Hihn, J., Juster, L., Johnson, J., Menzies, T., Michael, G.: Improving and expanding NASA software cost estimation methods. In: 2016 IEEE Aerospace Conference. IEEE (2016)Google Scholar
  12. 12.
    Briand, L.C., Emam, K.E., Surmann, D., Wieczorek, I., Maxwell, K.D.: An assessment and comparison of common software cost estimation modeling techniques. In: International Conference on Software Engineering, pp. 313–322 (1999)Google Scholar
  13. 13.
    Putnam, L.H.: A general empirical solution to the macro software sizing and estimating problem. IEEE Trans. Softw. Eng. SE-4(4), 345–361 (1978)CrossRefGoogle Scholar
  14. 14.
    Jorgensen, M.: What we do and don’t know about software development effort estimation. IEEE Softw. 31(2), 37–40 (2014)CrossRefGoogle Scholar
  15. 15.
    Wena, J., Lia, S., Linb, Z., Huc, Y., Huang, C.: Systematic literature review of machine learning-based software development effort estimation models. Inf. Softw. Technol. 54(1), 41–59 (2012)CrossRefGoogle Scholar
  16. 16.
    Jeffery, R., Ruhe, M., Wieczorek, I.: A comparative study of two software development cost modeling techniques using multi-organizational and company-specific data. Inf. Softw. Technol. 42(14), 1009–1016 (2000)CrossRefGoogle Scholar
  17. 17.
    Heiat, A.: Comparison of artificial neural network and regression models for estimating software development effort. Inf. Softw. Technol. 44(15), 911–922 (2002)CrossRefGoogle Scholar
  18. 18.
    Shepperd, M., Schofield, C.: Estimating software project effort using analogies. IEEE Trans. Softw. Eng. 23(11), 736–743 (1997)CrossRefGoogle Scholar
  19. 19.
    Gray, A.R., MacDonell, S.G.: A comparison of techniques for developing predictive models of software metrics. Inf. Softw. Technol. 39(6), 425–437 (1997)CrossRefGoogle Scholar
  20. 20.
    Walkerden, F., Jeffery, R.: An empirical study of analogy-based software effort estimation. Empir. Softw. Eng. 4(2), 135–158 (1999)CrossRefGoogle Scholar
  21. 21.
    Mendes, E., Watson, I., Triggs, C., Mosley, N., Counsell, S.: A comparative study of cost estimation models for web hypermedia applications. Empir. Softw. Eng. 8(2), 163–196 (2003)CrossRefGoogle Scholar
  22. 22.
    Briand, L.C., Emam, K.E., Surmann, D., Wieczorek, I., Maxwell, K.D.: An assessment and comparison of common software cost estimation modeling techniques. In: Proceedings of the 21st International Conference on Software Engineering, pp. 313–322 (1999)Google Scholar
  23. 23.
    Stensrud, E.: Alternative approaches to effort prediction of ERP projects. Inf. Softw. Technol. 43(7), 413–423 (2001)CrossRefGoogle Scholar
  24. 24.
    Jorgensen, M., Shepperd, M.: A systematic review of software development cost estimation studies. IEEE Trans. Softw. Eng. 33(1), 33–53 (2007)CrossRefGoogle Scholar
  25. 25.
    Minku, L.L., Yao, X.: How to make best use of cross-company data in software effort estimation? In: Proceedings of the 36th International Conference on Software Engineering, pp. 446–456 (2014)Google Scholar
  26. 26.
    Boehm, B.W.: Software Estimation with COCOMO II. Prentice-Hall, Upper Saddle River (2002)Google Scholar
  27. 27.
    Jones, C.: Estimating Software Costs: Bringing Realism to Estimating, 2nd edn. The McGraw-Hill Companies, New York (2007)Google Scholar
  28. 28.
    Prokopova, Z., Silhavy, P., Silhavy, R.: Influence analysis of selected factors in the function point work effort estimation. In: Intelligent Systems in Cybernetics and Automation Control Theory. CoMeSySo 2018 (2019)Google Scholar
  29. 29.
    Silhavy, P., Silhavy, R., Prokopová, Z.: Categorical variable segmentation model for software development effort estimation. IEEE Access PP, 1 (2019).  https://doi.org/10.1109/access.2019.2891878
  30. 30.
    Prokopova, Z., Silhavy, P., Silhavy, R.: VAF factor influence on the accuracy of the effort estimation provided by modified function points methods. In: Annals of DAAAM & Proceedings, vol. 29, pp. 0076–0084, 9 p (2018)Google Scholar
  31. 31.
    Seo, Y.-S., Yoon, K.-A., Bae, D.-H.: An empirical analysis of software effort estimation with outlier elimination. In: Proceedings of the 4th International Workshop on Predictor Models in Software Engineering, pp. 25–32. ACM, New York (2008).  https://doi.org/10.1145/1370788.1370796
  32. 32.
    Seo, Y.-S., Bae, D.-H.: On the value of outlier elimination on software effort estimation research. Empir. Softw. Eng. 18, 659–698 (2013).  https://doi.org/10.1007/s10664-012-9207-yCrossRefGoogle Scholar
  33. 33.
    Kitchenham, B., Mendes, E.: Software productivity measurement using multiple size measures. IEEE Trans. Softw. Eng. 30(12), 1023–1035 (2004)CrossRefGoogle Scholar
  34. 34.
    Kitchenham, B., MacDonell, S., Pickard, L., Shepperd, M.: What accuracy statistics really measure. IEE Proc. Softw. Eng. 148(3), 81–85 (2001)CrossRefGoogle Scholar
  35. 35.
    Foss, T., Stensrud, E., Kitchenham, B., Myrtveit, I.: A simulation study of the model evaluation criterion MMRE. IEEE Trans. Softw. Eng. 29(11), 985–995 (2003)CrossRefGoogle Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of Applied InformaticsTomas Bata University in ZlinZlinCzech Republic

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