Exploring the Role of Large Language Models in Automated Code Review and Software Quality Enhancement

As the newest, most savvy technology domain, Large Language Models (LLMs) are the technological force which is now revolutionizing Automated Code Review and Software Quality Assurance, making the context aware analysis, adaptive learning, and the intelligent recommendations. However, traditional code review methods are not only time consuming, but also prone to human error and are not scalable. The use of LLMs in software engineering allows for the detection of defects, optimization of performance and the maintenance of improve mental quality. In this study, we examine the path of automated code review, LLM capabilities and the effect they have on software quality. LLMs clearly have great advantages, but accuracy is still a concern, false positives abound, biases lurk, and computation can be a constraint. The research also identifies areas of future advancement on the hybrid AI-human review systems as well as improved LLM architectures that understand the code significantly better for more robust reviews.