THE ROLE OF ARTIFICIAL INTELLIGENCE (AI) IN SOFTWARE TESTING – A SYSTEMATIC LITERATURE REVIEW
TL;DR
How traditional and AI-powered tools are used in four critical areas test management, test case management, defect management, and version management is discussed and the study results prove that AI-powered testing is better than traditional testing in each of these areas.
Abstract
In today's fast-changing software landscape, the need for effective software testing has grown increasingly vital for guaranteeing quality, reliability, and security in the software-driven world. With the increasing capabilities of Artificial Intelligence (AI), it has the promise of overcoming the known shortcomings of traditional testing methods that are still largely manual, rule-based, and reactive in their approach to quality assurance. The scope of research conducted on AI tools in testing has covered diverse areas such as machine learning, deep learning, natural language processing, and generative AI, showing the potential of these tools in different testing tasks, and identifying some ongoing challenges. In this paper, we'll discuss how traditional and AI-powered tools are used in four critical areas test management, test case management, defect management, and version management and our study results prove that AI-powered testing is better than traditional testing in each of these areas.