AI Tools for Automating Code Reviews, Providing Contextual Feedback, and Improving the Efficiency of the Review Process
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V7I5P103Keywords:
Code review, artificial intelligence, static analysis, contextual feedback, machine learning, GitHub Copilot, AWS CodeGuru, NLP, software engineering, LLMsAbstract
Code review is an essential process in contemporary software development that guarantees code quality, safety, maintainability and teamwork. However, manual code reviews take a lot of time, are likely to fail because of mistakes, and depend on the reviewer's knowledge. As Artificial Intelligence (AI) becomes more popular, so does the trend toward automating and improving the code review process with Machine Learning (ML), Natural Language Processing (NLP), and Large Language Models (LLM). This paper explores the advancements in AI tools aimed at automating code review processes, improving efficiency, and delivering relevant feedback. It highlights platforms like GitHub Copilot, Codacy, DeepCode, and CodeGuru, and discusses the technologies underpinning them, including static code analysis and NLP techniques. The study tests a dynamic system combining static analysis and contextual review on a public software repository, measuring review accuracy, time savings, false positives, and developer satisfaction. Results indicate that AI-powered review tools can reduce the review cycle by 40-60% and provide insights comparable to human reviewers. Challenges include addressing complex logic, ensuring useful feedback, and integrating seamlessly into developer workflows. The findings suggest significant potential for AI tools in code review, contingent upon team oversight, and signal avenues for future research in areas such as explainable AI and ethical considerations
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