On the software projects' duration estimation using support vector regression

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28120%2F23%3A63564638" target="_blank" >RIV/70883521:28120/23:63564638 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/70883521:28140/23:63564638 RIV/70883521:28150/23:63564638

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-031-21435-6_25" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-21435-6_25</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-21435-6_25" target="_blank" >10.1007/978-3-031-21435-6_25</a>

Jazyk výsledku

angličtina

Název v původním jazyce

On the software projects' duration estimation using support vector regression

Popis výsledku v původním jazyce

Estimating the project’s duration is one of the critical steps in helping to ensure project success. It helps to allocate resources and personnel appropriately during project development. This study aims to look for a more suitable algorithm between two selected algorithms for estimating project duration. Two machine learning algorithms, Multiple Linear Regression and Support Vector Regression, were used to estimate the project’s duration. The data used here is an ISBSG dataset with intelligent preprocessing to give an ideal fit to the algorithm used. The dependent variables used in the test are project size, maximum team size, and resource level. With the two algorithms selected, the estimated value of the project&apos;s duration is relatively close to the actual duration of the project. Through the six evaluation criteria, R-square, MAE, MAPE, RMSE, MBRE, MIBRE and the pair-wise t-test statistical method, the Support Vector Regression algorithm gives a much better estimate of the project&apos;s duration than the Multiple Linear Regression algorithm. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Název v anglickém jazyce

On the software projects' duration estimation using support vector regression

Popis výsledku anglicky

Estimating the project’s duration is one of the critical steps in helping to ensure project success. It helps to allocate resources and personnel appropriately during project development. This study aims to look for a more suitable algorithm between two selected algorithms for estimating project duration. Two machine learning algorithms, Multiple Linear Regression and Support Vector Regression, were used to estimate the project’s duration. The data used here is an ISBSG dataset with intelligent preprocessing to give an ideal fit to the algorithm used. The dependent variables used in the test are project size, maximum team size, and resource level. With the two algorithms selected, the estimated value of the project&apos;s duration is relatively close to the actual duration of the project. Through the six evaluation criteria, R-square, MAE, MAPE, RMSE, MBRE, MIBRE and the pair-wise t-test statistical method, the Support Vector Regression algorithm gives a much better estimate of the project&apos;s duration than the Multiple Linear Regression algorithm. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

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

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

Lecture Notes in Networks and Systems

ISBN

978-3-031-21434-9

ISSN

23673370

e-ISSN

2367-3389

Počet stran výsledku

11

Strana od-do

288-298

Název nakladatele

Springer Science and Business Media Deutschland GmbH

Místo vydání

Berlín

Místo konání akce

on-line

Datum konání akce

10. 10. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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