Jaafar, Jafreezal and Gilal, Abdul Rehman and Omar, Mazni and Basri, Shuib and Abdul Aziz, Izzatdin and Hasan, Mohd Hilmi (2017) A rough-fuzzy inference system for selecting team leader for software development teams. In: Cybernetics Approaches in Intelligent Systems. Springer, Cham, pp. 304-314. ISBN 978-3-319-67617-3

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Abstract

Inappropriate team composition is one of the important factors that can impact the overall process of software development. Numerous models for team composition have also been suggested, yet they have been disapproved by the researchers and organisations for having ineffectiveness in yielding positive results. Therefore, this study proposes a rough-fuzzy model for selecting team leader for software development teams to avoid the limitations of individual techniques (i.e., Rough Set Theory (RST) or Fuzzy Set Theory (FST)). Moreover, the model development was divided into two portions: Decision Rules Development and Fuzzy Inference System (FIS) development. Johnson Algorithm (JA) was applied using ROSETTA toolkit under rough set theory principles for decision rule construction. Decision rules were then used under Mamdani’s fuzzy inference method. At the end, the developed model was validated based on the results of prediction accuracy and F1-measures.

Item Type:	Book Section
Uncontrolled Keywords:	Software development Team composition Rough set Fuzzy set Team lead Rule-based
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	School of Computing
Depositing User:	Mrs. Norazmilah Yaakub
Date Deposited:	26 Sep 2019 01:37
Last Modified:	26 Sep 2019 01:38
URI:	https://repo.uum.edu.my/id/eprint/26469

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