Finding an Effective Classification Technique to Develop a Software Team Composition Model

TL;DR

This study compares classification techniques to develop an effective software team composition model, finding that Rough Sets Theory with Johnson Algorithm provides high accuracy and practical decision rules for team member selection.

Contribution

It introduces a novel team composition model using RST and Johnson Algorithm, incorporating gender, personality, and role predictors for improved accuracy.

Findings

Johnson Algorithm of RST is most effective.

Developed 24 decision rules for team member selection.

Model improves prediction accuracy and reduces pattern complexity.

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 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.