Finding an effective classification technique to develop a software team composition model. Issue 1 (27th October 2017)

• Record Type: Journal Article
• Authors: Gilal, Abdul Rehman; Jaafar, Jafreezal; Capretz, Luiz Fernando; Omar, Mazni; Basri, Shuib; Aziz, Izzatdin Abdul
• Is Part Of: Journal of software. Volume 30:Issue 1(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-10-27
• Subjects: classification technique -- personality -- rough set -- software development -- team composition; Software engineering -- Periodicals; Computer software -- Development -- Periodicals; Software maintenance -- Periodicals; 005.1
• DOI: 10.1002/smr.1920
• Languages: English
• ISSNs: 2047-7473

Abstract

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 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.