Gilal, Abdul Rehman and Jaafar, Jafreezal and Capretz, Luiz Fernando and Omar, Mazni and Basri, Shuib and Abdul Aziz, Izzatdin (2017) Finding an effective classification technique to develop a software team composition model. Journal of Software: Evolution and Process, 30 (1). e1920. ISSN 20477473

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Abstract

Ineffective software team composition has become recognized as a prominent aspect of software project failures.Reports from results extracted from different theoretical personality models have produced contradicting fits, validity challenges, and missing guidance during software development personnel selection.It is also believed that the technique/s used while developing a model can impact the overall results.Thus, this study aims to: 1) discover an effective classification technique to solve the problem, and 2) develop a model for composition of the software development team.The model developed was composed of three predictors: team role, personality types, and gender variables; it also contained one outcome: team performance variable.The techniques used for model development were logistic regression, decision tree, and Rough Sets Theory (RST).Higher prediction accuracy and reduced patte rn complexity were the two parameters forselecting the effective technique.Based on the results, the Johnson Algorithm (JA) of RST appeared to be an effective technique for a team composition model.The study has proposed a set of 24 decision rules for finding effective team members.These rules involve gender classification to highlight the appropriate personality profile for software developers.In the end, this study concludes that selecting an appropriate classification technique is one of the most important factors in developing effective models.

Item Type:	Article
Uncontrolled Keywords:	software development, team composition, classification technique, personality, Rough set
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Divisions:	School of Computing
Depositing User:	Mrs. Norazmilah Yaakub
Date Deposited:	15 Feb 2018 01:27
Last Modified:	15 Feb 2018 01:27
URI:	https://repo.uum.edu.my/id/eprint/23355

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