Prediction of functional requirements classes in business information systems

Arbi Ghazarian

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Low predictability is a major concern in most software development endeavors as it implies high risk in terms of schedule, quality, and cost. Ontologies have received considerable attention in software engineering, as they afford predictive capabilities for various aspects of software domains, and as such, they can be employed as a basis for the development of more effective approaches to the engineering and management of software systems and projects. Ontologies, however, vary in terms of the comprehensiveness and accuracy of the predictions they make and, therefore, one must rigorously evaluate their predictive power before adopting them. This paper investigates the predictive power of an ontology that serves as a requirements domain model for Business Information Systems (BIS). Results from this study indicate that an accurate prediction of functional requirements categories in BIS is well within reach. This finding bears important implications for the advancement of domain-specific engineering of Business Information Systems.

Original languageEnglish (US)
Pages (from-to)142-157
Number of pages16
JournalWSEAS Transactions on Business and Economics
Volume10
Issue number3
StatePublished - 2013

Fingerprint

Business information
Prediction
Ontology
Information systems
Predictive power
Software development
Predictability
Systems software
Comprehensiveness
Costs
Schedule
Software engineering
Software

Keywords

  • Business information systems
  • Domain model
  • Empirical study
  • Functional requirements
  • Ontology

ASJC Scopus subject areas

  • Business and International Management
  • Economics and Econometrics
  • Finance

Cite this

Prediction of functional requirements classes in business information systems. / Ghazarian, Arbi.

In: WSEAS Transactions on Business and Economics, Vol. 10, No. 3, 2013, p. 142-157.

Research output: Contribution to journalArticle

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