Finding an Effective Classification Technique to Develop a Software Team Composition Model
It addresses software project failures due to poor team composition, offering a practical model for personnel selection, but is incremental as it compares existing classification techniques.
This study tackled the problem of ineffective software team composition by developing a model using predictors like team role, personality types, and gender to predict team performance, finding that the Johnson Algorithm of Rough Sets Theory was the most effective technique with higher prediction accuracy and reduced pattern complexity, resulting in 24 decision rules for selecting team members.
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 pattern complexity were the two 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.