A rough-fuzzy inference system for selecting team leader for software development teams
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 positi...
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| Main Authors: | , , , , , |
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| Format: | Book Section |
| Published: |
Springer
2017
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| Subjects: | |
| Online Access: | http://repo.uum.edu.my/26469/ http://doi.org/10.1007/978-3-319-67618-0_28 |
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| Institution: | Universiti Utara Malaysia |
| Summary: | 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. |
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