Kurtosis and Skewness Adjustment for Software Effort Estimation

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

Abstract

To avoid software development project failure, accurate estimation of software development effort is necessary at the beginning of a software project. This paper proposes to adjust the kurtosis and the skewness of project feature variables for better fitting of software estimation models. The proposed method conducts logarithmic transformation of variables, then conducts the kurtosis and skewness transformation to make the variable distribution closer to the normal distribution. To empirically evaluate the effectiveness of the proposed method, we employed three industry data sets and linear regression models with three-fold cross validation. The result of the evaluation showed that the models with the proposed method were better in both the goodness of fit and the estimation accuracy in terms of MMRE compared to log-log regression.

Original languageEnglish
Title of host publicationProceedings - 25th Asia-Pacific Software Engineering Conference, APSEC 2018
PublisherIEEE Computer Society
Pages504-511
Number of pages8
ISBN (Electronic)9781728119700
DOIs
Publication statusPublished - May 21 2019
Event25th Asia-Pacific Software Engineering Conference, APSEC 2018 - Nara, Japan
Duration: Dec 4 2018Dec 7 2018

Publication series

NameProceedings - Asia-Pacific Software Engineering Conference, APSEC
Volume2018-December
ISSN (Print)1530-1362

Conference

Conference25th Asia-Pacific Software Engineering Conference, APSEC 2018
CountryJapan
CityNara
Period12/4/1812/7/18

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Keywords

  • modeling
  • normal distribution
  • software metrics

ASJC Scopus subject areas

  • Software

Cite this

Fukui, S., Monden, A., & Yucel, Z. (2019). Kurtosis and Skewness Adjustment for Software Effort Estimation. In Proceedings - 25th Asia-Pacific Software Engineering Conference, APSEC 2018 (pp. 504-511). [8719446] (Proceedings - Asia-Pacific Software Engineering Conference, APSEC; Vol. 2018-December). IEEE Computer Society. https://doi.org/10.1109/APSEC.2018.00065