Propose-Specific Information Related to Prediction Level at x and Mean Magnitude of Relative Error: A Case Study of Software Effort Estimation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F22%3A63556518" target="_blank" >RIV/70883521:28140/22:63556518 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.mdpi.com/2227-7390/10/24/4649" target="_blank" >https://www.mdpi.com/2227-7390/10/24/4649</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/math10244649" target="_blank" >10.3390/math10244649</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Propose-Specific Information Related to Prediction Level at x and Mean Magnitude of Relative Error: A Case Study of Software Effort Estimation

Popis výsledku v původním jazyce

The prediction level at x (PRED(x)) and mean magnitude of relative error (MMRE) are measured based on the magnitude of relative error between real and predicted values. They are the standard metrics that evaluate accurate effort estimates. However, these values might not reveal the magnitude of over-/under-estimation. This study aims to define additional information associated with the PRED(x) and MMRE to help practitioners better interpret those values. We propose the formulas associated with the PRED(x) and MMRE to express the level of scatters of predictive values versus actual values on the left (sig(Left)), on the right (sig(Right)), and on the mean of the scatters (sig). We depict the benefit of the formulas with three use case points datasets. The proposed formulas might contribute to enriching the value of the PRED(x) and MMRE in validating the effort estimation.

Název v anglickém jazyce

Propose-Specific Information Related to Prediction Level at x and Mean Magnitude of Relative Error: A Case Study of Software Effort Estimation

Popis výsledku anglicky

The prediction level at x (PRED(x)) and mean magnitude of relative error (MMRE) are measured based on the magnitude of relative error between real and predicted values. They are the standard metrics that evaluate accurate effort estimates. However, these values might not reveal the magnitude of over-/under-estimation. This study aims to define additional information associated with the PRED(x) and MMRE to help practitioners better interpret those values. We propose the formulas associated with the PRED(x) and MMRE to express the level of scatters of predictive values versus actual values on the left (sig(Left)), on the right (sig(Right)), and on the mean of the scatters (sig). We depict the benefit of the formulas with three use case points datasets. The proposed formulas might contribute to enriching the value of the PRED(x) and MMRE in validating the effort estimation.

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

2022

Kód důvěrnosti údajů

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

Název periodika

Mathematics

ISSN

2227-7390

e-ISSN

2227-7390

Svazek periodika

10

Číslo periodika v rámci svazku

24

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

14

Strana od-do

1-14

Kód UT WoS článku

000904479300001

EID výsledku v databázi Scopus

2-s2.0-85144651757