Spectral clustering effect in software development effort estimation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F21%3A63537838" target="_blank" >RIV/70883521:28140/21:63537838 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.mdpi.com/2073-8994/13/11/2119" target="_blank" >https://www.mdpi.com/2073-8994/13/11/2119</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/sym13112119" target="_blank" >10.3390/sym13112119</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Spectral clustering effect in software development effort estimation

Popis výsledku v původním jazyce

Software development effort estimation is essential for software project planning and management. In this study, we present a spectral clustering algorithm based on symmetric matrixes as an option for data processing. It is expected that constructing an estimation model on more similar data can increase the estimation accuracy. The research methods employ symmetrical data processing and experimentation. Four experimental models based on function point analysis, stepwise regression, spectral clustering, and categorical variables have been conducted. The results indicate that the most advantageous variant is a combination of stepwise regression and spectral clustering. The proposed method provides the most accurate estimates compared to the baseline method and other tested variants. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Název v anglickém jazyce

Spectral clustering effect in software development effort estimation

Popis výsledku anglicky

Software development effort estimation is essential for software project planning and management. In this study, we present a spectral clustering algorithm based on symmetric matrixes as an option for data processing. It is expected that constructing an estimation model on more similar data can increase the estimation accuracy. The research methods employ symmetrical data processing and experimentation. Four experimental models based on function point analysis, stepwise regression, spectral clustering, and categorical variables have been conducted. The results indicate that the most advantageous variant is a combination of stepwise regression and spectral clustering. The proposed method provides the most accurate estimates compared to the baseline method and other tested variants. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

Symmetry-Basel

ISSN

2073-8994

e-ISSN

—

Svazek periodika

13

Číslo periodika v rámci svazku

11

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

22

Strana od-do

1-22

Kód UT WoS článku

000725345700001

EID výsledku v databázi Scopus

2-s2.0-85118952331