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Outliners detection method for software effort estimation models

Result description

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.

Keywords

Software development effort estimationStepwise regressionProduct delivery rateFunction point analysisOutliner detection

The result's identifiers

Alternative languages

  • Result language

    angličtina

  • Original language name

    Outliners detection method for software effort estimation models

  • Original language description

    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.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2019

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    SOFTWARE ENGINEERING METHODS IN INTELLIGENT ALGORITHMS, VOL 1

  • ISBN

    978-3-030-19806-0

  • ISSN

    2194-5357

  • e-ISSN

  • Number of pages

    11

  • Pages from-to

    444-455

  • Publisher name

    Springer

  • Place of publication

    Cham

  • Event location

    Praha

  • Event date

    Apr 24, 2019

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000503384000043

Basic information

Result type

D - Article in proceedings

D

OECD FORD

Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Year of implementation

2019