-
Je něco špatně v tomto záznamu ?
Algorithmic Optimisation Method for Improving Use Case Points Estimation
R. Silhavy, P. Silhavy, Z. Prokopova,
Jazyk angličtina Země Spojené státy americké
Typ dokumentu časopisecké články, práce podpořená grantem
NLK
Directory of Open Access Journals
od 2006
Free Medical Journals
od 2006
Public Library of Science (PLoS)
od 2006
PubMed Central
od 2006
Europe PubMed Central
od 2006
ProQuest Central
od 2006-12-01
Open Access Digital Library
od 2006-10-01
Open Access Digital Library
od 2006-01-01
Open Access Digital Library
od 2006-01-01
Medline Complete (EBSCOhost)
od 2008-01-01
Nursing & Allied Health Database (ProQuest)
od 2006-12-01
Health & Medicine (ProQuest)
od 2006-12-01
Public Health Database (ProQuest)
od 2006-12-01
ROAD: Directory of Open Access Scholarly Resources
od 2006
- MeSH
- algoritmy * MeSH
- interpretace statistických dat * MeSH
- lidé MeSH
- regresní analýza * MeSH
- software MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
This paper presents a new size estimation method that can be used to estimate size level for software engineering projects. The Algorithmic Optimisation Method is based on Use Case Points and on Multiple Least Square Regression. The method is derived into three phases. The first phase deals with calculation Use Case Points and correction coefficients values. Correction coefficients are obtained by using Multiple Least Square Regression. New project is estimated in the second and third phase. In the second phase Use Case Points parameters for new estimation are set up and in the third phase project estimation is performed. Final estimation is obtained by using newly developed estimation equation, which used two correction coefficients. The Algorithmic Optimisation Method performs approximately 43% better than the Use Case Points method, based on their magnitude of relative error score. All results were evaluated by standard approach: visual inspection, goodness of fit measure and statistical significance.
Citace poskytuje Crossref.org
- 000
- 00000naa a2200000 a 4500
- 001
- bmc16027983
- 003
- CZ-PrNML
- 005
- 20161005132532.0
- 007
- ta
- 008
- 161005s2015 xxu f 000 0|eng||
- 009
- AR
- 024 7_
- $a 10.1371/journal.pone.0141887 $2 doi
- 024 7_
- $a 10.1371/journal.pone.0141887 $2 doi
- 035 __
- $a (PubMed)26550835
- 040 __
- $a ABA008 $b cze $d ABA008 $e AACR2
- 041 0_
- $a eng
- 044 __
- $a xxu
- 100 1_
- $a Silhavy, Radek $u Faculty of Applied Informatics, Tomas Bata University in Zlin, Zlin, Czech Republic.
- 245 10
- $a Algorithmic Optimisation Method for Improving Use Case Points Estimation / $c R. Silhavy, P. Silhavy, Z. Prokopova,
- 520 9_
- $a This paper presents a new size estimation method that can be used to estimate size level for software engineering projects. The Algorithmic Optimisation Method is based on Use Case Points and on Multiple Least Square Regression. The method is derived into three phases. The first phase deals with calculation Use Case Points and correction coefficients values. Correction coefficients are obtained by using Multiple Least Square Regression. New project is estimated in the second and third phase. In the second phase Use Case Points parameters for new estimation are set up and in the third phase project estimation is performed. Final estimation is obtained by using newly developed estimation equation, which used two correction coefficients. The Algorithmic Optimisation Method performs approximately 43% better than the Use Case Points method, based on their magnitude of relative error score. All results were evaluated by standard approach: visual inspection, goodness of fit measure and statistical significance.
- 650 12
- $a algoritmy $7 D000465
- 650 12
- $a interpretace statistických dat $7 D003627
- 650 _2
- $a lidé $7 D006801
- 650 12
- $a regresní analýza $7 D012044
- 650 _2
- $a software $7 D012984
- 655 _2
- $a časopisecké články $7 D016428
- 655 _2
- $a práce podpořená grantem $7 D013485
- 700 1_
- $a Silhavy, Petr $u Faculty of Applied Informatics, Tomas Bata University in Zlin, Zlin, Czech Republic.
- 700 1_
- $a Prokopova, Zdenka $u Faculty of Applied Informatics, Tomas Bata University in Zlin, Zlin, Czech Republic.
- 773 0_
- $w MED00180950 $t PloS one $x 1932-6203 $g Roč. 10, č. 11 (2015), s. e0141887
- 856 41
- $u https://pubmed.ncbi.nlm.nih.gov/26550835 $y Pubmed
- 910 __
- $a ABA008 $b sig $c sign $y a $z 0
- 990 __
- $a 20161005 $b ABA008
- 991 __
- $a 20161005132919 $b ABA008
- 999 __
- $a ok $b bmc $g 1166297 $s 952613
- BAS __
- $a 3
- BAS __
- $a PreBMC
- BMC __
- $a 2015 $b 10 $c 11 $d e0141887 $e 20151109 $i 1932-6203 $m PLoS One $n PLoS One $x MED00180950
- LZP __
- $a Pubmed-20161005