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
Subtitle of host publicationProceedings of the 2008 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement
Pages312-314
Number of pages3
DOIs
Publication statusPublished - Dec 1 2008
Externally publishedYes
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

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Software
  • Electrical and Electronic Engineering

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  • 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). (ESEM'08: Proceedings of the 2008 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement). https://doi.org/10.1145/1414004.1414064