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

Fingerprint

Sampling

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

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: Chapter in Book/Report/Conference proceedingConference contribution

Kamei, Y, Keung, J, Monden, A & Matsumoto, KI 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, 2nd International Symposium on Empirical Software Engineering and Measurement, ESEM 2008, Kaiserslautern, Germany, 10/9/08. https://doi.org/10.1145/1414004.1414064
Kamei Y, Keung J, Monden A, Matsumoto KI. 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. 2008. p. 312-314 https://doi.org/10.1145/1414004.1414064
Kamei, Yasutaka ; Keung, Jacky ; Monden, Akito ; Matsumoto, Ken Ichi. / An over-sampling method for analogy-based software effort estimation. ESEM'08: Proceedings of the 2008 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement. 2008. pp. 312-314
@inproceedings{d66114a7c1a84d078364c4b4a3bda749,
title = "An over-sampling method for analogy-based software effort estimation",
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.",
keywords = "Analogy, Empirical study, Over-sampling, Software effort estimation",
author = "Yasutaka Kamei and Jacky Keung and Akito Monden and Matsumoto, {Ken Ichi}",
year = "2008",
doi = "10.1145/1414004.1414064",
language = "English",
isbn = "9781595939715",
pages = "312--314",
booktitle = "ESEM'08: Proceedings of the 2008 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement",

}

TY - GEN

T1 - An over-sampling method for analogy-based software effort estimation

AU - Kamei, Yasutaka

AU - Keung, Jacky

AU - Monden, Akito

AU - Matsumoto, Ken Ichi

PY - 2008

Y1 - 2008

N2 - 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.

AB - 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.

KW - Analogy

KW - Empirical study

KW - Over-sampling

KW - Software effort estimation

UR - http://www.scopus.com/inward/record.url?scp=62949141179&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=62949141179&partnerID=8YFLogxK

U2 - 10.1145/1414004.1414064

DO - 10.1145/1414004.1414064

M3 - Conference contribution

SN - 9781595939715

SP - 312

EP - 314

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

ER -