Feature weighting heuristics for analogy-based effort estimation models

Ayse Tosun, Burak Turhan, Ayse Basar Bener

Research output: Contribution to journalArticleResearchpeer-review

32 Citations (Scopus)

Abstract

Software cost estimation is one of the critical tasks in project management. In a highly demanding and competitive market environment, software project managers need robust models and methodologies to accurately predict the cost of a new project. Analogy-based cost estimation is one of the widely used models that rely on historical project data. It checks the similarity of features between past and current projects, and it approximates current project cost from past ones. One shortcoming of analogy-based cost estimation is that it assumes all project features as equal. However, these features may have different impacts on project cost based on their relevance. In this research, we present two feature weight assignment heuristics for cost estimation. We assign weights to the project features by benefiting from a statistical technique, namely principal components analysis (PCA) that is used for extracting optimal linear patterns of high dimensional data. We test our proposed heuristics on public datasets and conclude that the prediction performance in terms of MMRE and Pred(25) increases with a statistical-based assignment technique rather than random assignment approach.

Original languageEnglish
Pages (from-to)10325-10333
Number of pages9
JournalExpert Systems with Applications
Volume36
Issue number7
DOIs
Publication statusPublished - 1 Sep 2009
Externally publishedYes

Keywords

  • Analogy-based cost estimation
  • Principal components analysis
  • Software cost estimation

Cite this