An over-sampling method for analogy-based software effort estimation

Yasutaka Kamei, Jacky Keung, Akito Monden, Ken Ichi Matsumoto

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

3 Citations (Scopus)

Abstract

This paper proposes a novel method to generate synthetic project cases and add them to a fit dataset for the purpose of improving the performance of analogy-based software effort estimation. The proposed method extends conventional over-sampling method, which is a preprocessing procedure for n-group classification problems, which makes it suitable for any unbalanced dataset to be used in analogy-based system. We experimentally evaluated the effect of the over-sampling method to improve the performance of the analogy-based software effort estimation by using the Desharnais dataset. Results show significant improvement to the estimation accuracy by using our approach.

Original languageEnglish
Title of host publicationESEM'08: Proceedings of the 2008 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement
Pages312-314
Number of pages3
DOIs
Publication statusPublished - 2008
Externally publishedYes
Event2nd International Symposium on Empirical Software Engineering and Measurement, ESEM 2008 - Kaiserslautern, Germany
Duration: Oct 9 2008Oct 10 2008

Other

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

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Keywords

  • Analogy
  • Empirical study
  • Over-sampling
  • Software effort estimation

ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Electrical and Electronic Engineering

Cite this

Kamei, Y., Keung, J., Monden, A., & Matsumoto, K. I. (2008). An over-sampling method for analogy-based software effort estimation. In ESEM'08: Proceedings of the 2008 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement (pp. 312-314) https://doi.org/10.1145/1414004.1414064