TY  - JOUR
N2  - 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 3 predictors: team role, personality types, and gender variables; it also contained 1 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 pattern complexity were the 2 parameters for selecting 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. © 2017 John Wiley & Sons, Ltd.
SN  - 20477481
Y1  - 2018///
KW  - Decision theory; Decision trees; Personnel selection; Rough set theory; Software engineering
KW  -  Classification technique; Gender classification; Logistic regressions; Personality; Prediction accuracy; Software developer; Software development teams; Team composition
KW  -  Software design
IS  - 1
TI  - Finding an effective classification technique to develop a software team composition model
A1  - Gilal, A.R.
A1  - Jaafar, J.
A1  - Capretz, L.F.
A1  - Omar, M.
A1  - Basri, S.
A1  - Aziz, I.A.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042867165&doi=10.1002%2fsmr.1920&partnerID=40&md5=e6594dc1484859f10d4d007f49f5799a
N1  - cited By 24
ID  - scholars10892
PB  - John Wiley and Sons Ltd
JF  - Journal of Software: Evolution and Process
AV  - none
VL  - 30
ER  -