AI Powered Code Review Assistant

Alok Anand; Anish Kumar; Akash Babu NJ; Deepak Kumar; Varshitha M K1

1

Publication Date: 2024/12/20

Abstract: 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.

Keywords: AI, Code Review, Generative AI, ChatGPT, GeminiAPI, Software Development.

DOI: https://doi.org/10.5281/zenodo.14533537

PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT24DEC156.pdf

REFERENCES

  1. M. Coutinho, L. Marquez, and F. Wang, “Evaluating the Code Quality of AI-Assisted Code Generation Tools: An   Empirical   Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT,” arXiv, 2023. Available: https://ar5iv.labs.arxiv.org/html/2312.10868
  2. T. R. McIntosh, T. Susanto, and L. Brown, ”From   Google Gemini to OpenAI Q: A Survey on Gen- erative AI,” 2023. Available: https://alok180202- my.sharepoint.com
  3. Odeh, N. Odeh, and R. Ahmed, ”A Compara- tive Review of AI Techniques for Automated Code Generation,” Journal of Recent AI Advancements, vol. 13, no. 1, pp. 726-739, 2023.
  4. R. Ferdiana, ”The Impact of Artificial Intelligence on Programmer Productivity,” ResearchGate, 2024. Available: https://www.researchgate.net
  5. Amazon Web Services, ”Amazon EC2 User Guide for Linux Instances,” AWS Documentation. Avail- able: https://docs.aws.amazon.com
  6. Pallets Projects,”Flask API Documen-tation,” Flask Documentation. Available: https://flask.palletsprojects.com/en/stable/api/
  7. GitHub,Inc.,   ”GitHub   API   Docu- mentation,” GitHub Docs. Available: https://docs.github.com/en/rest
  8. OpenAI,”ChatGPT API     Documenta- tion,” OpenAI Documentation. Available: https://platform.openai.com/docs/overview
  9. Google,”Google Gemini API Documen- tation,” Google Documentation. Available: https://cloud.google.com/gemini/docs
  10. Microsoft, ”VS Code Docu- mentation,” Microsoft. Available: https://code.visualstudio.com/docs