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Decision-Tree Models for Predicting Time Performance in Software-Intensive Projects

Decision-Tree Models for Predicting Time Performance in Software-Intensive Projects

Nermin Sökmen, Ferhan Çebi
Copyright: © 2017 |Volume: 8 |Issue: 2 |Pages: 23
ISSN: 1938-0232|EISSN: 1938-0240|EISBN13: 9781522512004|DOI: 10.4018/IJITPM.2017040105
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MLA

Sökmen, Nermin, and Ferhan Çebi. "Decision-Tree Models for Predicting Time Performance in Software-Intensive Projects." IJITPM vol.8, no.2 2017: pp.64-86. http://doi.org/10.4018/IJITPM.2017040105

APA

Sökmen, N. & Çebi, F. (2017). Decision-Tree Models for Predicting Time Performance in Software-Intensive Projects. International Journal of Information Technology Project Management (IJITPM), 8(2), 64-86. http://doi.org/10.4018/IJITPM.2017040105

Chicago

Sökmen, Nermin, and Ferhan Çebi. "Decision-Tree Models for Predicting Time Performance in Software-Intensive Projects," International Journal of Information Technology Project Management (IJITPM) 8, no.2: 64-86. http://doi.org/10.4018/IJITPM.2017040105

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Abstract

Initial requirements, new requirements and technical issues are the main factors that have a great effect over the software product development process. Difficulties resulting from incomprehensibility of initial requirements indicate two sub-factors: Deviations determined during analysis of initial requirements and deviations resulting from interpretation of requirements inaccurate and incomplete. New requirements being received from customers or end users during the development process affect the project performance. There can be problems during the implementation of product specifications, inaccurate formation of architectural design and technical solutions, incorrect coding of functions, or wrong realization of interfaces. The general technical problems cover the all problems arising from technical reasons and the negative situations they create on the project. During the design and implementation activities of software intensive projects, these tree main factors can be affected by other sub-factors. The aim of this study is to examine the factor classes which influence these three problem domains with CHAID (Chi-squared Automatic Interaction Detection) technique. Time deviations caused by initial requirements, new requirements and general technical problems are selected as target variables. In this research, 75 projects that develop software intensive products are studied to formalize the most accurate decision mechanism.

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