Skip to main content
SpringerLink
Log in

Outliners Detection Method for Software Effort Estimation Models

  • Conference paper
  • First Online:
Software Engineering Methods in Intelligent Algorithms (CSOC 2019)

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

Included in the following conference series:

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Silhavy, R., Silhavy, P., Prokopova, Z.: Evaluating subset selection methods for use case points estimation. Inf. Softw. Technol. 97, 1–9 (2018)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  8. Nassif, A., et al.: Neural network models for software development effort estimation: a comparative study. Neural Comput. Appl. 27(8), 2369–2381 (2016)

    Article  Google Scholar 

  9. Azzeh, M., Nassif, A.B.: Project productivity evaluation in early software effort estimation. J. Softw.: Evol. Process 30, e2110 (2018)

    Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    MATH  Google Scholar 

  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. ISBSG: ISBSG Development & Enhancement Repository – Release 13. International Software Benchmarking Standards Group (ISBSG) (2015)

    Google Scholar 

  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)

    Article  Google Scholar 

  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. de Myttenaere, A., Golden, B., Le Grand, B., Rossi, F.: Mean absolute percentage error for regression models. Neurocomputing 192, 38–48 (2016)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Applied Informatics, Tomas Bata University in Zlin, nam. T.G.M 5555, 76001, Zlin, Czech Republic

    Petr Silhavy, Radek Silhavy & Zdenka Prokopova

Authors
  1. Petr Silhavy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Radek Silhavy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zdenka Prokopova
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Radek Silhavy .

Editor information

Editors and Affiliations

  1. Faculty of Applied Informatics, Tomas Bata University in ZlĂ­n, ZlĂ­n, Czech Republic

    Radek Silhavy

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Silhavy, P., Silhavy, R., Prokopova, Z. (2019). Outliners Detection Method for Software Effort Estimation Models. In: Silhavy, R. (eds) Software Engineering Methods in Intelligent Algorithms. CSOC 2019. Advances in Intelligent Systems and Computing, vol 984. Springer, Cham. https://doi.org/10.1007/978-3-030-19807-7_43

Download citation

Publish with us

Policies and ethics

Search

Navigation