Evaluating software cost estimation models using particle swarm optimisation and fuzzy logic for NASA projects: A comparative study

Alaa F. Sheta*, Aladdin Ayesh, David Rine

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)

Abstract

Bidding for contracts depends mainly on estimated costs of a given project, which makes an accurate estimation of effort and time required very important with great impact on budget computation and project success. Inaccurate estimates are likely lead to one or all of the following negative outcomes: failure in making a profit, increased probability of incomplete project and delay of project delivery date. In this paper, we provide a comparison between models developed for software cost estimation using particle swarm optimisation (PSO) algorithm, fuzzy logic (FL), and well-known cost estimation models such as Halstead, Walston- Felix, Bailey-Basili and Doty models. The performance of the developed models is evaluated based on the mean magnitude of relative error (MMRE) for NASA software projects.

Original languageEnglish
Pages (from-to)365-373
Number of pages9
JournalInternational Journal of Bio-Inspired Computation
Volume2
Issue number6
DOIs
Publication statusPublished - 2010
Externally publishedYes

Keywords

  • Bailey-Basili
  • Doty
  • FL
  • Fuzzy logic
  • Halstead
  • NASA
  • Particle swarm optimisation
  • PSO
  • Software cost estimation
  • Walston-Felix

Fingerprint

Dive into the research topics of 'Evaluating software cost estimation models using particle swarm optimisation and fuzzy logic for NASA projects: A comparative study'. Together they form a unique fingerprint.

Cite this