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Outliners Detection Method for Software Effort Estimation Models

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

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.

Keywords

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

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

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

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