AUTOMATED TESTING BASED ON ARTIFICIAL INTELLIGENCE

Abstract

This paper explores the application of artificial intelligence (AI) technologies in the automation of software testing processes. With the increasing complexity of software systems and the shortening of product delivery timelines, traditional testing approaches are becoming less effective. The use of AI opens up new opportunities to optimize testing processes, improve flexibility, and reduce costs. The article analyzes key technologies such as machine learning, neural networks, natural language processing, and intelligent decision-making systems. It also provides an overview of modern AI-based testing tools, including Testim.io, Applitools, Functionize, and Mabl. The main advantages of integrating AI into QA include automatic test generation, adaptability to changes, detection of complex defects, and expanded coverage. At the same time, certain challenges are discussed, such as the "black box" problem, the demand for high-quality data, implementation costs, and the difficulty of supporting unstable interfaces. The conclusion outlines promising directions such as the use of generative models, Explainable AI, and the development of autonomous testing agents. The analysis shows that the integration of AI into testing can make the process more intelligent, efficient, and adaptive, with great potential for the future.