Outcome prediction of software projects for information technology vendors

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Several studies indicate that roughly 70% of the projects based on the software development have resulted in failure, thereby researchers and practitioners have been tried to develop solutions that will improve project success rates. It is insisted that to raise success rates, support should be provided by the organization to which the projects belong. With the aid of predictions that incorporate project outcomes for various information technology (IT) vendors, this study aims at identifying projects that should be preferentially supported by an organization. The data of 332 projects of various Japanese IT vendors were collected using an Internet survey, and a success/failure prediction algorithm is created by employing the Bayes classifier technique on the collected data. A resultant algorithm with 77.3% prediction capability was obtained. It is expected that the success/failure prediction procedure, including the prediction algorithm, help significantly to specify projects that an organization needs to participate in as priority.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2017
PublisherIEEE Computer Society
Pages1733-1737
Number of pages5
Volume2017-December
ISBN (Electronic)9781538609484
DOIs
Publication statusPublished - 2018 Feb 9
Event2017 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2017 - Singapore, Singapore
Duration: 2017 Dec 102017 Dec 13

Other

Other2017 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2017
CountrySingapore
CitySingapore
Period17/12/1017/12/13

Keywords

  • information technology vendor
  • software development
  • success/failure prediction

ASJC Scopus subject areas

  • Business, Management and Accounting (miscellaneous)
  • Industrial and Manufacturing Engineering
  • Safety, Risk, Reliability and Quality

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