Prediction accuracy measurements as a fitness function for software effort estimation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F15%3A43873633" target="_blank" >RIV/70883521:28140/15:43873633 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1186/s40064-015-1555-9" target="_blank" >http://dx.doi.org/10.1186/s40064-015-1555-9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1186/s40064-015-1555-9" target="_blank" >10.1186/s40064-015-1555-9</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Prediction accuracy measurements as a fitness function for software effort estimation

Popis výsledku v původním jazyce

This paper evaluates the usage of analytical programming and different fitness functions for software effort estimation. Analytical programming and differential evolution generate regression functions. These functions are evaluated by the fitness function which is part of differential evolution. The differential evolution requires a proper fitness function for effective optimization. The problem is in proper selection of the fitness function. Analytical programming and different fitness functions were tested to assess insight to this problem. Mean magnitude of relative error, prediction 25 %, mean squared error (MSE) and other metrics were as possible candidates for proper fitness function. The experimental results shows that means squared error performs best and therefore is recommended as a fitness function. Moreover, this work shows that analytical programming method is viable method for calibrating use case points method. All results were evaluated by standard approach: visual insp

Název v anglickém jazyce

Prediction accuracy measurements as a fitness function for software effort estimation

Popis výsledku anglicky

This paper evaluates the usage of analytical programming and different fitness functions for software effort estimation. Analytical programming and differential evolution generate regression functions. These functions are evaluated by the fitness function which is part of differential evolution. The differential evolution requires a proper fitness function for effective optimization. The problem is in proper selection of the fitness function. Analytical programming and different fitness functions were tested to assess insight to this problem. Mean magnitude of relative error, prediction 25 %, mean squared error (MSE) and other metrics were as possible candidates for proper fitness function. The experimental results shows that means squared error performs best and therefore is recommended as a fitness function. Moreover, this work shows that analytical programming method is viable method for calibrating use case points method. All results were evaluated by standard approach: visual insp

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

C - Předmět řešení projektu podléhá obchodnímu tajemství (§ 504 Občanského zákoníku), ale název projektu, cíle projektu a u ukončeného nebo zastaveného projektu zhodnocení výsledku řešení projektu (údaje P03, P04, P15, P19, P29, PN8) dodané do CEP, jsou upraveny tak, aby byly zveřejnitelné.

Název periodika

SpringerPlus

ISSN

2193-1801

e-ISSN

—

Svazek periodika

4

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

17

Strana od-do

1-17

Kód UT WoS článku

000368718000002

EID výsledku v databázi Scopus

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