Outliners Detection Method for Software Effort Estimation Models

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 984)


Outliner detection methods are studied as an approach for simulated in-house dataset creation. In-house datasets are understood as an approach for increasing the estimation accuracy of the functional points-based estimation models. The method which was selected as the best option for outliners’ detection is the median absolute deviation. The product delivery rate was used as a parameter for the median absolution deviation method. The estimation accuracy was compared for a public dataset and simulated in-house datasets, using stepwise regression models. Results show that in-house datasets increase estimation accuracy.


Outliner detection Function point analysis Product delivery rate Stepwise regression Software development effort estimation 


  1. 1.
    Silhavy, R., Silhavy, P., Prokopova, Z.: Evaluating subset selection methods for use case points estimation. Inf. Softw. Technol. 97, 1–9 (2018)CrossRefGoogle Scholar
  2. 2.
    Azzeh, M., Nassif, A.B., Banitaan, S.: Comparative analysis of soft computing techniques for predicting software effort based use case points. Iet Softw. 12, 19–29 (2018)CrossRefGoogle Scholar
  3. 3.
    Borandag, E., Yucalar, F., Erdogan, S.Z.: A case study for the software size estimation through MK II FPA and FP methods. Int. J. Comput. Appl. Technol. 53, 309–314 (2016)CrossRefGoogle Scholar
  4. 4.
    Celar, S., Mudnic, E., Kalajdzic, E.: Software size estimating method based on MK II FPA 1.3 unadjusted. In: Annals of DAAAM for 2009 and 20th International DAAAM Symposium “Intelligent Manufacturing and Automation: Focus on Theory, Practice and Education”, Vienna, pp. 1939–1940 (2009)Google Scholar
  5. 5.
    Prokopova, Z., Silhavy, R., Silhavy, P.: The effects of clustering to software size estimation for the use case points methods. Adv. Intell. Syst. Comput. 575, 479–490 (2017)Google Scholar
  6. 6.
    Azzeh, M., Nassif, A.B., Banitaan, S.: Comparative analysis of soft computing techniques for predicting software effort based use case points. IET Softw. 12, 19–29 (2017)CrossRefGoogle Scholar
  7. 7.
    Azzeh, M., Nassif, A.B.: A hybrid model for estimating software project effort from Use Case Points. Appl. Soft Comput. 49, 981–989 (2016)CrossRefGoogle Scholar
  8. 8.
    Nassif, A., et al.: Neural network models for software development effort estimation: a comparative study. Neural Comput. Appl. 27(8), 2369–2381 (2016)CrossRefGoogle Scholar
  9. 9.
    Azzeh, M., Nassif, A.B.: Project productivity evaluation in early software effort estimation. J. Softw.: Evol. Process 30, e2110 (2018)Google Scholar
  10. 10.
    Urbanek, T., Kolcavova, A., Kuncar, A.: Inferring productivity factor for use case point method. In: 28th DAAAM International Symposium on Intelligent Manufacturing and Automation, DAAAM 2017, pp. 597–601 (2017)Google Scholar
  11. 11.
    Agouris, P., Mountrakis, G., Stefanidis, A.: Automated spatiotemporal change detection in digital aerial imagery. In: Automated Geo-Spatial Image and Data Exploitation, vol. 4054, pp. 2–13. International Society for Optics and Photonics (2000)Google Scholar
  12. 12.
    Alameddine, I., Kenney, M.A., Gosnell, R.J., Reckhow, K.H.: Robust multivariate outlier detection methods for environmental data. J. Environ. Eng. 136, 1299–1304 (2010)CrossRefGoogle Scholar
  13. 13.
    Seo, Y.S., Bae, D.H.: On the value of outlier elimination on software effort estimation research. Empir. Softw. Eng. 18, 659–698 (2013)CrossRefGoogle Scholar
  14. 14.
    Malinowski, E.R.: Determination of rank by median absolute deviation (DRMAD): a simple method for determining the number of principal factors responsible for a data matrix. J. Chemometr. 23, 1–6 (2009)CrossRefGoogle Scholar
  15. 15.
    Chen, C., Liu, L.-M.: Joint estimation of model parameters and outlier effects in time series. J. Am. Stat. Assoc. 88, 284–297 (1993)zbMATHGoogle Scholar
  16. 16.
    Rousseeuw, P.J., Hubert, M.: Robust statistics for outlier detection. Wiley Interdisciplinary Rev.: Data Mining Knowl. Discov. 1, 73–79 (2011)Google Scholar
  17. 17.
    ISBSG: ISBSG Development & Enhancement Repository – Release 13. International Software Benchmarking Standards Group (ISBSG) (2015)Google Scholar
  18. 18.
    Silhavy, R., Silhavy, P., Prokopova, Z.: Analysis and selection of a regression model for the Use Case Points method using a stepwise approach. J. Syst. Softw. 125, 1–14 (2017)CrossRefGoogle Scholar
  19. 19.
    Silhavy, P., Silhavy, R., Prokopova, Z.: Evaluation of data clustering for stepwise linear regression on use case points estimation. Adv. Intell. Syst. Comput. 575, 491–496 (2017)Google Scholar
  20. 20.
    de Myttenaere, A., Golden, B., Le Grand, B., Rossi, F.: Mean absolute percentage error for regression models. Neurocomputing 192, 38–48 (2016)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

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

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