AI Powered Code Review Assistant

With the increasing complexity of software systems, maintaining high code quality is essential to ensure reliability, maintainability, and security. Traditionally, code reviews havebeen a manual and time- consuming process, often resulting in inconsistencies and missed issues due to human error. Recent advance- ments in artificial intelligence, specifically generative AI models like OpenAI’s Chat- GPT and Google Gemini, have opened new possibilities for automating code reviews by providing real-time, intelligent feedback on code quality. This survey paper explores the current state ofAI- assisted code review tools, focusing on the potential of generative AI models to improve software development workflows. We exam- ine the methodologies, benefits, and limita- tions of existing tools such as GitHub Copilot, Amazon CodeWhisperer, and other AI-drivensolutions. Additionally, we discuss the archi- tecture and design of an AI-powered code review assistant that integrates seamlessly with popular development environments like VS Code, leveraging cloud-based processing through AWS. Our findings suggest that integrating gener- ative AI into the code review process can significantly reduce review time, improve consistency, and enhance developer produc- tivity. This paper also highlightsthe cost-effective implementation of AI models in code reviews, demonstrating the feasibility of deploying scalable, budget-friendly solu- tions in real-world applications. By analyz- ing the strengths and weaknesses of current approaches, we outline the path for futureadvancements in AI-powered code review systems, focusing on multi- language support, enhanced security analysis, and continuouslearning capabilities.