Prioritizing key success factors of software projects using fuzzy AHP. Issue 1 (11th September 2017)

• Record Type: Journal Article
• Authors: Yaghoobi, Tahere
• Is Part Of: Journal of software. Volume 30:Issue 1(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-09-11
• Subjects: constrained fuzzy AHP -- key success factors -- project management -- software development; Software engineering -- Periodicals; Computer software -- Development -- Periodicals; Software maintenance -- Periodicals; 005.1
• DOI: 10.1002/smr.1891
• Languages: English
• ISSNs: 2047-7473

Abstract

Abstract: The main purpose of most software projects is to build an effective and efficient product that satisfies the user's requirements under constraints of basic available resources. Thus, recognizing the critical features that may affect developing the application could be imperative. In this study, we apply the fuzzy analytic hierarchy process (AHP) to the issue of ordering key success factors (KSFs) for software development projects. To do this, we first simplified the constrained fuzzy AHP method, and then from systematic literature reviews, a preliminary list of potential KSFs that influences software development projects was identified and compiled. Subsequently, on the basis of the criteria of the frequency of citation in previous studies, closely related and similar functionality, the preliminary list was consolidated into a final set of 15 possible KSFs and considered at 4 perspectives of project management, development team, product, and processes. Pairwise comparisons, required by fuzzy AHP method, were done by 22 experienced professional internal stakeholders to determine the preference weights of factors and perspectives in terms of natural language expressions. The results reveal that project management has the highest importance in comparison with other 3 perspectives, and the factor of efficient project management skills/methodologies is the major. The procedure used in this paper is simple and accurate to implement and concludes both relative weights and rankings of alternatives in a fuzzy environment.