Influence of outliers on analogy based software development effort estimation

Kenichi Ono, Masateru Tsunoda, Akito Monden, Kenichi Matsumoto

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

3 Citations (Scopus)

Abstract

In a software development project, project management is indispensable, and effort estimation is one of the important factors on the management. To improve estimation accuracy, outliers are often removed from dataset used for estimation. However, the influence of the outliers to the estimation accuracy is not clear. In this study, we added outliers to dataset experimentally, to analyze the influence. In the analysis, we changed the percentage of outliers, the extent of outliers, variable including outliers, and location of outliers on the dataset. After that, effort was estimated using the dataset. In the experiment, the influence of outliers was not very large, when they were included in the software size metric, the percentage of outliers was 10%, and the extent of outliers was 100%.

Original languageEnglish
Title of host publication2016 IEEE/ACIS 15th International Conference on Computer and Information Science, ICIS 2016 - Proceedings
EditorsKuniaki Uehara, Masahide Nakamura
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509008063
DOIs
Publication statusPublished - Aug 23 2016
Event15th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2016 - Okayama, Japan
Duration: Jun 26 2016Jun 29 2016

Publication series

Name2016 IEEE/ACIS 15th International Conference on Computer and Information Science, ICIS 2016 - Proceedings

Other

Other15th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2016
Country/TerritoryJapan
CityOkayama
Period6/26/166/29/16

Keywords

  • abnormal value
  • case based reasoning
  • effort prediction

ASJC Scopus subject areas

  • Computer Science(all)
  • Energy Engineering and Power Technology
  • Control and Optimization

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