Authors :
Alok Anand; Anish Kumar; Akash Babu NJ; Deepak Kumar; Varshitha M K
Volume/Issue :
Volume 9 - 2024, Issue 12 - December
Google Scholar :
https://tinyurl.com/2krapt75
Scribd :
https://tinyurl.com/yck6pvy3
DOI :
https://doi.org/10.5281/zenodo.14533537
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.
References :
- 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
- 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
- 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.
- R. Ferdiana, ”The Impact of Artificial Intelligence on Programmer Productivity,” ResearchGate, 2024. Available: https://www.researchgate.net
- Amazon Web Services, ”Amazon EC2 User Guide for Linux Instances,” AWS Documentation. Avail- able: https://docs.aws.amazon.com
- Pallets Projects,”Flask API Documen-tation,” Flask Documentation. Available: https://flask.palletsprojects.com/en/stable/api/
- GitHub,Inc., ”GitHub API Docu- mentation,” GitHub Docs. Available: https://docs.github.com/en/rest
- OpenAI,”ChatGPT API Documenta- tion,” OpenAI Documentation. Available: https://platform.openai.com/docs/overview
- Google,”Google Gemini API Documen- tation,” Google Documentation. Available: https://cloud.google.com/gemini/docs
- Microsoft, ”VS Code Docu- mentation,” Microsoft. Available: https://code.visualstudio.com/docs
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.