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 personne...
Main Authors: | , , , , , |
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Format: | Article |
Language: | English |
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John Wiley & Sons, Inc
2017
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Subjects: | |
Online Access: | https://repo.uum.edu.my/id/eprint/23355/1/JSEP%2029%2010%202017%201%2022.pdf |
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author | Gilal, Abdul Rehman Jaafar, Jafreezal Capretz, Luiz Fernando Omar, Mazni Basri, Shuib Abdul Aziz, Izzatdin |
author_facet | Gilal, Abdul Rehman Jaafar, Jafreezal Capretz, Luiz Fernando Omar, Mazni Basri, Shuib Abdul Aziz, Izzatdin |
author_sort | Gilal, Abdul Rehman |
collection | UUM |
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 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. |
first_indexed | 2024-07-04T06:23:20Z |
format | Article |
id | uum-23355 |
institution | Universiti Utara Malaysia |
language | English |
last_indexed | 2024-07-04T06:23:20Z |
publishDate | 2017 |
publisher | John Wiley & Sons, Inc |
record_format | dspace |
spelling | uum-233552018-02-15T01:27:18Z https://repo.uum.edu.my/id/eprint/23355/ Finding an effective classification technique to develop a software team composition model Gilal, Abdul Rehman Jaafar, Jafreezal Capretz, Luiz Fernando Omar, Mazni Basri, Shuib Abdul Aziz, Izzatdin QA76 Computer software 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. John Wiley & Sons, Inc 2017 Article PeerReviewed application/pdf en https://repo.uum.edu.my/id/eprint/23355/1/JSEP%2029%2010%202017%201%2022.pdf 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 http://doi.org/10.1002/smr.1920 doi:10.1002/smr.1920 doi:10.1002/smr.1920 |
spellingShingle | QA76 Computer software Gilal, Abdul Rehman Jaafar, Jafreezal Capretz, Luiz Fernando Omar, Mazni Basri, Shuib Abdul Aziz, Izzatdin Finding an effective classification technique to develop a software team composition model |
title | 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_short | 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 |
topic | QA76 Computer software |
url | https://repo.uum.edu.my/id/eprint/23355/1/JSEP%2029%2010%202017%201%2022.pdf |
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