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Analysis and selection of a regression model for the Use Case Points method using a stepwise approach

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dc.title Analysis and selection of a regression model for the Use Case Points method using a stepwise approach en
dc.contributor.author Šilhavý, Radek
dc.contributor.author Šilhavý, Petr
dc.contributor.author Prokopová, Zdenka
dc.relation.ispartof Journal of Systems and Software
dc.identifier.issn 0164-1212 Scopus Sources, Sherpa/RoMEO, JCR
dc.date.issued 2017
utb.relation.volume 125
dc.citation.spage 1
dc.citation.epage 14
dc.type article
dc.language.iso en
dc.publisher Elsevier
dc.identifier.doi 10.1016/j.jss.2016.11.029
dc.relation.uri https://www.sciencedirect.com/science/article/pii/S016412121630231X
dc.subject Dataset en
dc.subject Multiple linear regression en
dc.subject Software size estimation en
dc.subject Stepwise approach en
dc.subject Use Case Points en
dc.subject Variables analysis en
dc.description.abstract This study investigates the significance of use case points (UCP) variables and the influence of the complexity of multiple linear regression models on software size estimation and accuracy. Stepwise multiple linear regression models and residual analysis were used to analyse the impact of model complexity. The impact of each variable was studied using correlation analysis. The estimated size of software depends mainly on the values of the weights of unadjusted UCP, which represent a number of use cases. Moreover, all other variables (unadjusted actors' weights, technical complexity factors, and environmental complexity factors) from the UCP method also have an impact on software size and therefore cannot be omitted from the regression model. The best performing model (Model D) contains an intercept, linear terms, and squared terms. The results of several evaluation measures show that this model's estimation ability is better than that of the other models tested. Model D also performs better when compared to the UCP model, whose Sum of Squared Error was 268,620 points on Dataset 1 and 87,055 on Dataset 2. Model D achieved a greater than 90% reduction in the Sum of Squared Errors compared to the Use Case Points method on Dataset 1 and a greater than 91% reduction on Dataset 2. The medians of the Sum of Squared Errors for both methods are significantly different at the 95% confidence level (p < 0.01), while the medians for Model D (312 and 37.26) are lower than Use Case Points (3134 and 3712) on Datasets 1 and 2, respectively. © 2016 The Authors en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1006799
utb.identifier.obdid 43877547
utb.identifier.scopus 2-s2.0-85000774224
utb.identifier.wok 000395359500001
utb.identifier.coden JSSOD
utb.source j-scopus
dc.date.accessioned 2017-02-28T15:11:27Z
dc.date.available 2017-02-28T15:11:27Z
dc.rights Attribution-NonCommercial-NoDerivs 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.access openAccess
utb.contributor.internalauthor Šilhavý, Radek
utb.contributor.internalauthor Šilhavý, Petr
utb.contributor.internalauthor Prokopová, Zdenka
utb.fulltext.affiliation Radek Silhavy ∗ , Petr Silhavy, Zdenka Prokopova Tomas Bata University in Zlin, Faculty of Applied Infomatics, Nad Stranemi 4511, 76001, Zlin, Czech Republic Corresponding author. E-mail addresses: rsilhavy@fai.utb.cz (R. Silhavy), psilhavy@fai.utb.cz (P. Silhavy), prokopova@fai.utb.cz (Z. Prokopova).
utb.fulltext.dates Received 3 March 2016 Revised 15 November 2016 Accepted 18 November 2016 Available online 22 November 2016
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