Automation Testing Suitability Checking for Software Projects - A Machine Learning Based Model

Abstract

Software testing is an activity conducted to test the software. It has two approaches; manual testing and automation testing. Automation testing is an approach of software testing in which programming scripts are written to automate the process of testing. There are some software projects for which automation testing is suitable and other requires manual testing. In this research, we address the problem of selecting suita ble software projects for adoption of automation testing. We develop a machine learning based model to predict the suitability of automation testing for software projects with the help of significant factors; project complexity, project cost, project time, project domain, team automation skills, project team size, functionality important over user interface design, design important over user interface fu11ctio11ality, project target audience and project requirements nature. Our proposed model is based on PART classifier possessing 933 accuracy.