Abstract
Code review is an important process in the software development lifecycle to ensure high-quality, maintainable, and efficient code. Over time, automated code review systems have evolved from manual methods to include advanced techniques such as dynamic analysis, machine learning, deep learning, and, more recently, generative artificial intelligence. These techniques use natural language processing and predictive models to provide faster and more accurate code reviews with intelligent, context-sensitive feedback. This survey explores the evolution of automated code review, starting with traditional methods and moving toward the latest AI-powered approaches. We discuss the methods used, the challenges faced, and the benefits these systems offer. In addition, we highlight future trends and examine how AI is transforming code review processes to meet the needs of modern software development.