Prediction of failures in the project management knowledge areas using a machine learning approach for software companies

Abstract

In this paper we propose a novel machine-learning model to predict project management knowledge areas failure for software companies using ten knowledge areas in project management based solely on the criteria of unambiguity, measurability, consistency, and practicability. The majority of software projects fail in software companies due to a lack of software project managers who are unfamiliar with the Project Management Knowledge Areas (PMKAs) that are used without considering the company's conditions or project contexts. By distributing questionnaires, we use an experimental methodology and the snowball sampling method to collect data from software businesses. We employ machine learning techniques including Support Vector Machines (92.13%), Decision Trees (90%), K-Nearest Neighbors (87.64%), Logistic Regression (76.4%), and Naive Bayes (66%) to adapt data from failed software projects. When we look at the results, Support Vector Machine outperforms the other four machine learning methods. High dimensional data is more efficient and contains nonlinear changes since Support Vector Machines deal with categorical data. The study's purpose is to improve project quality and decrease software project failure. Finally, we recommend collecting more failed project datasets from software businesses and comparing them to our findings to predict knowledge domain failure.