Evaluation of data clustering for stepwise linear regression on use case points estimation

Citation data:

Advances in Intelligent Systems and Computing, ISSN: 2194-5357, Vol: 575, Page: 491-496

Publication Year:
2017
Usage 141
Abstract Views 141
Citations 2
Citation Indexes 2
Repository URL:
http://publikace.k.utb.cz/handle/10563/1007392; http://hdl.handle.net/10563/1007392
DOI:
10.1007/978-3-319-57141-6_52
Author(s):
Šilhavý, Petr; Šilhavý, Radek; Prokopová, Zdenka
Publisher(s):
Springer Nature; Springer Verlag
Tags:
Engineering; Computer Science; Clustering; Effort estimation; Parametric model; Stepwise linear regression; Use case points
conference paper description
In this paper, stepwise linear regression model in conjunction with clustering for effort estimation is investigated. Effect of clustering is compared to Use Case Points model. The 2 to 20 clusters were tested. As shown increasing a number of clusters brings lower prediction errors. More clusters lower a distance between clusters members, which allows to construct more capable stepwise linear regression model.