A Rule-Based Approach for Discovering Effectivesoftware Team Composition

Authors

  • Abdul Rehman Gilal Sukkur Institute of Business Administration, Pakistan
  • Mazni Omar School of Computing, Universiti Utara Malaysia, Malaysia
  • Kamal Imran Sharif School of Technology Management & Logistic, Universiti Utara Malaysia, Malaysia

DOI:

https://doi.org/10.32890/jict2014.13.1

Keywords:

Software team composition, personality types, diversity, team roles, rule-based

Abstract

Human aspects in software engineering play a key role in composing effective team members. However, to date there is no general consensus on the effective personality types and diversity based on software team roles. Thus, this paper aims to discover the effective personality types and diversity based on two software team roles – team leader and programmer by using a rule-based approach. The rule-based approach by employing the rough set technique was used to discover patterns of the data selected. In this study, four main steps were involved to discover the patterns – reduct generation rules, rules generation, rules fi ltering, and rules evaluation. The results show that the rules generated achieved acceptable prediction accuracy with more than 70 per cent accuracy. In addition, the ROC value achieved 0.65, which indicates the rule-based model is valid and useful. The results reveal that the extrovert personality type is dominant for both software team roles and a homogeneous or heterogeneous team plays an equal role to determine an effective team. This study provides useful rules for decision makers to understand and get insight into selecting effective team members that lead to producing high quality software.

 

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Published

19-02-2014

Issue

Vol. 13 (2014): Journal of Information and Communication Technology (JICT) Vol. 13, 2014

Section

Articles

How to Cite

Gilal, A. R., Omar, M., & Sharif, K. I. (2014). A Rule-Based Approach for Discovering Effectivesoftware Team Composition. Journal of Information and Communication Technology, 13, 1-20. https://doi.org/10.32890/jict2014.13.1

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Harvested 2026-09-06
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Identifiers DOI 10.32890/jict2014.13.1

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