AI-Driven Continuous Integration: Automating Code Review and Deployment with LLMs

Abstract

The integration of a Large Language Models (LLMs) into Continuous Integration (CI) pipelines greatly enhances the efficiency of the software development process. AI-based CI improves code quality by decreasing integration failure rates by 30 % and deployment time by 40 %. These models help in identifying bugs, enforcing coding standards, writing unit tests, and optimizing release management. But AIdriven CI comes with risks of security vulnerabilities, model bias and the need for human supervision. This paper uses case studies, surveys, and interviews to explore the effects of AI on CI/CD pipelines and identifies the pros and cons. The results of this search show that the use of AI in CI improves effectiveness, but it needs better oversight to avoid risks and improve model performance.