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Classification model for predicting cost slippage in governmental ICT projects

Published: 13 April 2015 Publication History

Abstract

In this paper we present a classification model for predicting cost slippage using data mining techniques. The model uses the initial planning of an ICT project in terms of budget and schedule and then predicts the category of cost slippage of the project. Three categories are distinguished where low slippage is considered normal, medium slippage requires attention, and large slippage requires action. The model was trained and validated with two data sets from the US Federal IT Dashboard and the Dutch Rijks-ICT-Dashboard, respectively that hold project data for large governmental ICT projects. The classification model is intended as a project management tool for sponsors and managers of large ICT projects in the public sector.

References

[1]
Beñat Bilbao-Osorio, Soumitra Dutta, Bruno Lanvin. "The Global Information Technology Report 2013. Growth and Jobs in a Hyperconnected World." 2013.
[2]
Replicated Survey of IT Software Project Failures, Khaled El Emam, A. Gonus Koru, 2008.
[3]
Australian Government 1. (2012). Australian Government ICT expenditure 2008-09-2009-10. Department of Finance and Deregulation.
[4]
Ministry of Finance. (2011). Finnish Government ICT Review 2010. Ministry of Finance publications 32b/2011 - Juvenes Print.
[5]
R Agrawal, T Imielinski, A Swami, Mining association rules between sets of items in large databases ACM SIGMOD Record, 1993.
[6]
Mining the Dutch National ICT Dashboard: SIG Analysis Report, Jeroen Arnoldus, Joost Visser, 2011.

Cited By

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  • (2024)Diverse Bagging Effort Estimation Model for Software Development ProjectComputational Science and Its Applications – ICCSA 202410.1007/978-3-031-64608-9_19(293-310)Online publication date: 2-Jul-2024
  • (2023)An Ensemble-Based Framework to Estimate Software Project Effort2023 IEEE 8th International Conference On Software Engineering and Computer Systems (ICSECS)10.1109/ICSECS58457.2023.10256337(47-52)Online publication date: 25-Aug-2023
  • (2021)Software Project Management Using Machine Learning Technique—A ReviewApplied Sciences10.3390/app1111518311:11(5183)Online publication date: 2-Jun-2021
  • Show More Cited By

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cover image ACM Conferences
SAC '15: Proceedings of the 30th Annual ACM Symposium on Applied Computing
April 2015
2418 pages
ISBN:9781450331968
DOI:10.1145/2695664
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 April 2015

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Author Tags

  1. ICT project analysis
  2. association rules
  3. classification model
  4. clustering
  5. empirical studies in software engineering

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  • Short-paper

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SAC 2015
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SAC 2015: Symposium on Applied Computing
April 13 - 17, 2015
Salamanca, Spain

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SAC '15 Paper Acceptance Rate 291 of 1,211 submissions, 24%;
Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

View all
  • (2024)Diverse Bagging Effort Estimation Model for Software Development ProjectComputational Science and Its Applications – ICCSA 202410.1007/978-3-031-64608-9_19(293-310)Online publication date: 2-Jul-2024
  • (2023)An Ensemble-Based Framework to Estimate Software Project Effort2023 IEEE 8th International Conference On Software Engineering and Computer Systems (ICSECS)10.1109/ICSECS58457.2023.10256337(47-52)Online publication date: 25-Aug-2023
  • (2021)Software Project Management Using Machine Learning Technique—A ReviewApplied Sciences10.3390/app1111518311:11(5183)Online publication date: 2-Jun-2021
  • (2020)Design and Development of Machine Learning Technique for Software Project Risk Assessment - A Review2020 8th International Conference on Information Technology and Multimedia (ICIMU)10.1109/ICIMU49871.2020.9243459(354-362)Online publication date: 24-Aug-2020

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