Utilizing Machine Learning for Predicting Software Faults Through Selenium Testing Tool

Abstract

Software quality assurance, especially in the context of the testing phase, plays a pivotal role in ensuring the reliability and functionality of software systems. Automation testing is recognized as a valuable technique to enhance test coverage and accuracy. However, challenges such as diverse automation tools and unrealistic expectations can hold up its effectiveness. This research explores the integration of machine learning into the Selenium automation testing tool to predict faults based on UI and historical scenarios. The study aims to investigate the impact of machine learning on perceived task difficulty and time required for fault prediction during software testing. The literature review emphasizes the importance of software testing, automation testing, and the Selenium tool. The research methodology employs a mixed-methods approach, combining quantitative and qualitative analyses. The results show positive perceptions regarding the clarity of implementing machine learning-based Selenium but mixed opinions on the ease of implementation. The ML-based Selenium tool demonstrates increased effectiveness, reliability, and reduced testing duration. Interviews highlight the complementary roles of manual and automated testing. The discussion addresses improved test effectiveness, reliability, challenges, and future considerations, affirming the viability and advantages of incorporating machine learning into the Selenium framework for automation testing.