LSA-X

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

Passakorn Phannachitta, Akito Monden, Jacky Keung, Kenichi Matsumoto

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)151-162
Number of pages12
JournalIEICE Transactions on Information and Systems
VolumeE99D
Issue number1
DOIs
Publication statusPublished - Jan 1 2016

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Productivity

Keywords

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

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Software
  • Artificial Intelligence
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition

Cite this

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: Contribution to journalArticle

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