Authors

Ananya Sharma

Student, AIT CSE Chandigarh University, Gharuan, Mohali, Punjab, India

Ravi Kumar

Student, AIT CSE Chandigarh University, Gharuan, Mohali, Punjab, India

Neha Gupta

Student, AIT CSE Chandigarh University, Gharuan, Mohali, Punjab, India

Amit Verma

Student, AIT CSE Chandigarh University, Gharuan, Mohali, Punjab, India

Abstract

This paper introduces an AI-Driven Code Review System that automates the evaluation and enhancement of code quality through the use of Artificial Intelligence (AI) and Machine Learning (ML). The system utilizes the MERN stack (MongoDB, Express.js, React.js, Node.js) to create a web-based platform that assists developers in efficiently identifying syntax errors, performance issues, and security vulnerabilities. By incorporating AI models such as GPT-based APIs alongside static analysis tools (ESLint, JSHint, SonarQube), the system delivers immediate feedback, optimization recommendations, and coding best practices. This platform boosts developer productivity, encourages standardized coding practices, minimizes manual review time, and supports ongoing improvements in software quality across both academic and industrial settings.

Keywords

AI Machine Learning MERN Stack Code Review Software Quality Natural Language Processing Automation Software Engineering

Citation of this Article

Ananya Sharma, Ravi Kumar, Neha Gupta, & Amit Verma. (2026). AI-Driven Automated Code Review System Using MERN Stack and Machine Learning Techniques. International Current Journal of Engineering and Science (ICJES), 5(2), 5-9. Article DOI: https://doi.org/10.47001/ICJES/2026.502002

Licence Copyright (c) 2026 International Current Journal of Engineering and Science. This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International Licence.

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