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Evaluation of Data Clustering for Stepwise Linear Regression on Use Case Points Estimation

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Software Engineering Trends and Techniques in Intelligent Systems (CSOC 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 575))

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Abstract

In this paper, stepwise linear regression model in conjunction with clustering for effort estimation is investigated. Effect of clustering is compared to Use Case Points model. The 2 to 20 clusters were tested. As shown increasing a number of clusters brings lower prediction errors. More clusters lower a distance between clusters members, which allows to construct more capable stepwise linear regression model.

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Authors and Affiliations

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

    Petr Silhavy, Radek Silhavy & Zdenka Prokopova

Authors
  1. Petr Silhavy
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  2. Radek Silhavy
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  3. Zdenka Prokopova
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Corresponding author

Correspondence to Petr Silhavy .

Editor information

Editors and Affiliations

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

    Radek Silhavy

  2. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlin, Czech Republic

    Petr Silhavy

  3. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlin, Czech Republic

    Zdenka Prokopova

  4. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlin, Czech Republic

    Roman Senkerik

  5. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlin, Czech Republic

    Zuzana Kominkova Oplatkova

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Silhavy, P., Silhavy, R., Prokopova, Z. (2017). Evaluation of Data Clustering for Stepwise Linear Regression on Use Case Points Estimation. In: Silhavy, R., Silhavy, P., Prokopova, Z., Senkerik, R., Kominkova Oplatkova, Z. (eds) Software Engineering Trends and Techniques in Intelligent Systems. CSOC 2017. Advances in Intelligent Systems and Computing, vol 575. Springer, Cham. https://doi.org/10.1007/978-3-319-57141-6_52

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  • DOI: https://doi.org/10.1007/978-3-319-57141-6_52

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57140-9

  • Online ISBN: 978-3-319-57141-6

  • eBook Packages: EngineeringEngineering (R0)

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