Towards adaptive soft computing based software effort prediction

Moshood Omolade Saliu*, Moataz Ahmed, Jarallah AlGhamdi

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

10 Scopus citations

Abstract

Algorithmic effort prediction models are limited by their inability to cope with imprecision present in software projects early in the development life cycle. A critical survey carried out by Saliu and Ahmed [17] reveals the lack of adaptive soft computing based effort prediction systems that provide complete transparency to the prediction system building strategies. In addition, efforts to model the imprecision problem in one of the most widely used algorithmic model, COCOMO, have not been appropriate [17]. The components of COCOMO model were addressed independently. Integrating the individual component into a single prediction system remains an open question. In this paper, we present a transparent and adaptive fuzzy logic framework for effort prediction based on COCOMO. The training strategies we have implemented in the framework tolerates imprecision, explains prediction rationale through rules, incorporates experts knowledge, and could adapt to a new environment as new data becomes available. Validation of the framework potentials has been carried out on artificial datasets and the COCOMO public database of completed projects.

Original languageEnglish
Pages16-21
Number of pages6
DOIs
StatePublished - 2004

Keywords

  • COCOMO
  • Effort prediction
  • Fuzzy logic
  • Soft computing

ASJC Scopus subject areas

  • General Computer Science
  • General Mathematics

Fingerprint

Dive into the research topics of 'Towards adaptive soft computing based software effort prediction'. Together they form a unique fingerprint.

Cite this