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The Effects of Clustering to Software Size Estimation for the Use Case Points Methods

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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

The main objective of the paper is to present the suitability and effects of several different clustering methods for improving accuracy of software size estimation. For software size estimation was used the Algorithmic Optimisation Method (AOM), which is based Use Case Points (UCP) method. The comparison of K-means, Hierarchical and Density-based clustering is provided. Gap, Silhouette and Calinski-Harabasz criterion were selected as an evaluation criterion for clustering quality. Estimation ability of clustered model is compared on Sum of squared error (SSE). Results shows that clustering improves an estimation ability.

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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

    Zdenka Prokopova, Radek Silhavy & Petr Silhavy

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

Correspondence to Zdenka Prokopova .

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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Prokopova, Z., Silhavy, R., Silhavy, P. (2017). The Effects of Clustering to Software Size Estimation for the Use Case Points Methods. 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_51

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  • DOI: https://doi.org/10.1007/978-3-319-57141-6_51

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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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