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
Result code in IS VaVaI
Result on the web
https://link.springer.com/chapter/10.1007/978-3-030-19807-7_43
DOI - Digital Object Identifier
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
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Czech description
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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
OECD FORD
Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Year of implementation
2019