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

    19 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

    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