Finding an effective classification technique to develop a software team composition model

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 personn...

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Main Authors: Gilal, A.R., Jaafar, J., Capretz, L.F., Omar, M., Basri, S., Aziz, I.A.
Format: Article
Published: John Wiley and Sons Ltd 2018
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042867165&doi=10.1002%2fsmr.1920&partnerID=40&md5=e6594dc1484859f10d4d007f49f5799a
http://eprints.utp.edu.my/21360/
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Institution: Universiti Teknologi Petronas
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spelling my.utp.eprints.213602018-09-25T06:37:01Z Finding an effective classification technique to develop a software team composition model Gilal, A.R. Jaafar, J. Capretz, L.F. Omar, M. Basri, S. Aziz, I.A. 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. John Wiley and Sons Ltd 2018 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042867165&doi=10.1002%2fsmr.1920&partnerID=40&md5=e6594dc1484859f10d4d007f49f5799a Gilal, A.R. and Jaafar, J. and Capretz, L.F. and Omar, M. and Basri, S. and Aziz, I.A. (2018) Finding an effective classification technique to develop a software team composition model. Journal of Software: Evolution and Process, 30 (1). http://eprints.utp.edu.my/21360/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description 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.
format Article
author Gilal, A.R.
Jaafar, J.
Capretz, L.F.
Omar, M.
Basri, S.
Aziz, I.A.
spellingShingle Gilal, A.R.
Jaafar, J.
Capretz, L.F.
Omar, M.
Basri, S.
Aziz, I.A.
Finding an effective classification technique to develop a software team composition model
author_facet Gilal, A.R.
Jaafar, J.
Capretz, L.F.
Omar, M.
Basri, S.
Aziz, I.A.
author_sort Gilal, A.R.
title Finding an effective classification technique to develop a software team composition model
title_short Finding an effective classification technique to develop a software team composition model
title_full Finding an effective classification technique to develop a software team composition model
title_fullStr Finding an effective classification technique to develop a software team composition model
title_full_unstemmed Finding an effective classification technique to develop a software team composition model
title_sort finding an effective classification technique to develop a software team composition model
publisher John Wiley and Sons Ltd
publishDate 2018
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042867165&doi=10.1002%2fsmr.1920&partnerID=40&md5=e6594dc1484859f10d4d007f49f5799a
http://eprints.utp.edu.my/21360/
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