The Evolving Role of Artificial Intelligence in Software Testing: Prospects and Challenges

Abstract

The ability of a computer to replicate human intellect in areas such as learning, reasoning, problem-solving, and decision-making
is known as artificial intelligence (AI). Among the many tools used in AI are rule-based systems, expert systems, neural
networks, and machine learning. AI makes use of algorithms and procedures to analyze data, derive inferences from laws and
patterns, and improve performance gradually. Software testing is the procedure by which the anticipated functioning of a software
program or product is examined and confirmed. Bug avoidance, reduced development costs, and enhanced performance
are some of testing’s advantages. Artificial intelligence (AI) has the potential to revolutionize software testing by automating test
script creation, test data generation, and test generation. This would not only improve the product quality but also streamline
the manual testing operations. It takes a lot of time, effort, and energy to test software. In an effort to improve both quality
and delivery time, automation solutions have been developed to aid in the automation of certain processes within the testing
process. Automation solutions have a diminishing return on investment (ROI) when CI/CD pipelines are integrated. Because
AI can scan code for errors and bugs far faster than humans and without human interaction, the testing community is turning
to AI to fill the void. Our goal in doing this research is to get a better understanding of how AI is influencing different STLC
activities and software testing components. Some of the most significant difficulties encountered by software testers while using
AI in testing are also attempted to be identified and explained in the research. The paper goes on to list a number of major ways
AI may improve software testing.