Fit data selection for software effort estimation models

Koji Toda, Akito Monden, Ken Ichi Matsumoto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

To construct a better multivariate regression model for software effort estimation, this paper proposes a method to select projects as a fit data from a given project data set based on estimation target's features. While regression models were often constructed from all available project data, this paper showed the necessity of fit data selection, and showed that the proposed method is one of the effective and systematic means to do the selection.

Original languageEnglish
Title of host publicationESEM'08
Subtitle of host publicationProceedings of the 2008 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement
Pages360-361
Number of pages2
DOIs
Publication statusPublished - Dec 1 2008
Event2nd International Symposium on Empirical Software Engineering and Measurement, ESEM 2008 - Kaiserslautern, Germany
Duration: Oct 9 2008Oct 10 2008

Publication series

NameESEM'08: Proceedings of the 2008 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement

Other

Other2nd International Symposium on Empirical Software Engineering and Measurement, ESEM 2008
CountryGermany
CityKaiserslautern
Period10/9/0810/10/08

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Keywords

  • Effort estimation
  • Multivariate regression

ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Electrical and Electronic Engineering

Cite this

Toda, K., Monden, A., & Matsumoto, K. I. (2008). Fit data selection for software effort estimation models. In ESEM'08: Proceedings of the 2008 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement (pp. 360-361). (ESEM'08: Proceedings of the 2008 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement). https://doi.org/10.1145/1414004.1414084