LSA-X : Exploiting productivity factors in linear size adaptation for analogy-based software effort estimation

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

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Author(s)

  • Passakorn Phannachitta
  • Akito Monden
  • Jacky Keung
  • Kenichi Matsumoto

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)151-162
Journal / PublicationIEICE Transactions on Information and Systems
VolumeE99D
Issue number1
Publication statusPublished - 1 Jan 2016

Abstract

Analogy-based software effort estimation has gained a considerable amount of attention in current research and practice. Its excellent estimation accuracy relies on its solution adaptation stage, where an effort estimate is produced from similar past projects. This study proposes a solution adaptation technique named LSA-X that introduces an approach to exploit the potential of productivity factors, i.e., project variables with a high correlation with software productivity, in the solution adaptation stage. The LSA-X technique tailors the exploitation of the productivity factors with a procedure based on the Linear Size Adaptation (LSA) technique. The results, based on 19 datasets show that in circumstances where a dataset exhibits a high correlation coefficient between productivity and a related factor (r ≥ 0.30), the proposed LSA-X technique statistically outperformed (95% confidence) the other 8 commonly used techniques compared in this study. In other circumstances, our results suggest using any linear adaptation technique based on software size to compensate for the limitations of the LSA-X technique.

Research Area(s)

  • Adaptation, Analogy, Empirical experiments, Productivity factor, Software development effort estimation

Citation Format(s)

LSA-X : Exploiting productivity factors in linear size adaptation for analogy-based software effort estimation. / Phannachitta, Passakorn; Monden, Akito; Keung, Jacky; Matsumoto, Kenichi.

In: IEICE Transactions on Information and Systems, Vol. E99D, No. 1, 01.01.2016, p. 151-162.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal