Design and Development of an AI-Powered Code Review Assistant

DOI

https://www.doi.org/10.59256/ijire.20260702027

Published In

Volume 7, Issue 2 (2026)

Published Online

March-April 2026

Pages

229-234

Article Type

Original Article

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The blistering development of software engineering has created more complex codebases, dispersed teams, and quicker development pipelines. Code review is still a important aspect of software quality assurance in such environments. Manual code reviews are however usually time consuming, inconsistent and restricted to human availability. The paper introduces an AI-driven code review helper that is based on GitHub pull request processes. The system receives the events of pull requests through webhooks, interprets the code diffs, and uses a Large Language Model (LLM) to come up with structured and contextually sensitive review recommendations. The assistant detects maintainability, performance, security, readability, and code standard problems. FastAPI is used to implement the backend system, and a Next.js dashboard allows visualizing code quality metrics and issue distributions. As the experimental outcomes show, the system is efficient to minimize the effort of reviewing and enhance the quality of feedback as well as to stay scalable in case of high loading conditions. The suggested solution indicates the opportunities of AI-aided tools to improve the contemporary software development processes.