Project Management Effort Estimation Using Agile Manager game platform

Effort estimation research studied the effort spent in direct software project development. While the effort involved in indirect software project development increased, management is considered as an indirect effort, but it influenced the overall effort since many foundations prefer buying Software rather than building it. The goal of this research paper is to study the effect of manager personality and task nature on management effort. To achieve our aims, we relied on a dataset that consists of detailed data, which forms distinct decision situations, methods and outcomes from 1,144 participants from distinct backgrounds. The dataset was gathered from an interactive game called the Agile Manager Game platform (AMG). The variables utilized in our dataset were manager personality (age, education and gender) and task difficulty. We applied a statistical analysis to our dataset using IBM SPSS which is flexible and relies on machine learning techniques, and we utilized different classifiers of different types. These classifiers are Naïve bayes, IBK, Decision tree J48 and AdaBoostM1. We applied also k-means clustering approach to our dataset. These classification and clustering techniques were applied using the well-known toll which is called WEKA. The results showed that task nature and manager education are considered significant determinants of management effort. However, manager personality age and gender do not affect management effort.