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

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)

3 Scopus Citations
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Author(s)

  • Yasutaka Kamei
  • Jacky Keung
  • Akito Monden
  • Ken-Ichi Matsumoto

Detail(s)

Original languageEnglish
Title of host publicationESEM'08: Proceedings of the 2008 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement
Pages312-314
Publication statusPublished - 2008
Externally publishedYes

Conference

Title2nd International Symposium on Empirical Software Engineering and Measurement, ESEM 2008
PlaceGermany
CityKaiserslautern
Period9 - 10 October 2008

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. Copyright 2008 ACM.

Research Area(s)

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

Citation Format(s)

An over-sampling method for analogy-based software effort estimation. / Kamei, Yasutaka; Keung, Jacky; Monden, Akito; Matsumoto, Ken-Ichi.

ESEM'08: Proceedings of the 2008 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement. 2008. p. 312-314.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)