AI-Based Intelligent Code Review System for Students and IT Professionals

Abstract

Modern software development demands rigorous code quality assurance, yet conventional manual review methods remain slow, inconsistent, and reliant on reviewer expertise. This paper presents an AI-Based Intelligence Code Review System that automates source code analysis through the integration of Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP). The system detects syntax errors, logical defects, and coding standard violations while delivering real-time, context-aware feedback to users. A multi-module architecture encompasses an AI code editor, bug detection engine, performance and security analysis components, and an adaptive learning assistant. Evaluation against manual review and static analysis baselines demonstrates that the proposed system achieves 94% accuracy, 92% precision, 91% recall, and an F1-score of 91%, outperforming existing approaches. The platform serves both educational and professional contexts, bridging the gap between automated error detection and continuous skill development.

Introduction

The document proposes an AI-Based Intelligence Code Review System that automates software code analysis using AI, ML, and NLP to overcome limitations of traditional peer review and static analysis tools, which are slow, inconsistent, and dependent on reviewer expertise.

It introduces a six-stage pipeline: preprocessing, tokenization, syntax analysis, semantic analysis, performance evaluation, and AI-based feedback generation. A key component, the Review Necessity Chain (RNC), filters trivial submissions to reduce unnecessary LLM usage, improving efficiency and achieving an 86% reduction in meaningless review calls.

The system is built as a full-stack platform with:

• A React/Next.js frontend code editor
• Node.js and FastAPI backend
• AI engine using LLMs and ML models
• MongoDB/MySQL databases on AWS infrastructure

Key modules include:

• AI code editor with real-time feedback
• AI mentorship assistant for learning support
• Performance analytics dashboard
• Structured learning paths and coding challenges
• Notifications and personalized career roadmap

The methodology details how submitted code is cleaned, tokenized, checked for syntax and semantic errors, analyzed for performance and security issues, and then processed by an LLM for natural-language suggestions. Results are displayed to users and stored for tracking and personalization.

Evaluation shows the system outperforms manual review, rule-based systems, and static tools, achieving about 94% accuracy. It also shows strong semantic alignment (~0.80 BERTScore) with expert feedback and improved error detection rates, especially in requirement violations and unnecessary code detection.

Conclusion

This paper presented an AI-Based Intelligence Code Review System that automates and augments the code review process for both educational and professional software engineering contexts. The multi-module platform — encompassing a six-stage analysis pipeline, Review Necessity Chain, security vulnerability detector, and adaptive learning assistant — addresses limitations identified across representative prior works in the literature. Quantitative evaluation confirms 94% accuracy and a 91% F1-score, outperforming manual review, static analysis, and rule-based approaches. BERTScore analysis validates that AI-generated feedback aligns with human expert reviews at a mean semantic similarity of 0.8001. The RNC mechanism achieves an 86% blocking rate for unnecessary reviews, reducing operational overhead in large-scale deployments. The proposed system improves code quality by automating the review process and delivering accurate, context-aware feedback, making it suitable for deployment across academic, competitive, and industrial software engineering environments. Future integration with CI/CD pipelines and advanced deep learning models will further strengthen the platform’s capabilities and broaden its applicability.

References

[1] D.-K. Lee and I. Joe, “A GPT-Based Code Review System with Accurate Feedback for Programming Education,” IEEE Access, vol. 13, pp. 105724–105737, 2025, doi: 10.1109/ACCESS.2025.3581139. [2] A. Burujwale, D. Goyal, K. Ghuge, A. Sapate, V. Gosavi, and S. Sanap, “AI-Powered Code Editors: Bridging the Gap Between Academia and Industry,” in Proc. 6th IEEE INCET, Bangalore, India, May 2025, doi: 10.1109/INCET64471.2025.11139961. [3] C. Adapa, S. S. Avulamanda, A. A. R. K, and A. Victor, “AI-Powered Code Review Assistant for Streamlining Pull Request Merging,” in Proc. IEEE ICWITE 2024, pp. 323–327, doi: 10.1109/ICWITE59797.2024.10503540. [4] A. Bacchelli and C. Bird, “Expectations, Outcomes, and Challenges of Modern Code Review,” in Proc. 35th ICSE, 2013, pp. 712–721. [5] M. Jovanovic and M. Campbell, “Generative Artificial Intelligence: Trends and Prospects,” IEEE Trans. Emerg. Top. Comput., vol. 10, no. 3, pp. 552–565, 2022. [6] Z. Li et al., “Automating Code Review Activities by Large-Scale Pre-Training,” in Proc. ACM ESEC/FSE, 2022, pp. 1035–1047. [7] T. M. N. Nimraka et al., “Agentic AI for Automated Pull Request Code Review,” in Proc. IEEE SLAAI-ICAI, 2025. [8] P. J. S. Xuan and Y. Lee, “Automated Code Review and Bug Detection Using Machine Learning,” in Proc. IEEE ICSCA, 2024. [9] P. Halvadia and J. Anvik, “Code Reviewer Recommendation Using Machine Learning and Data Mining,” in Proc. IEEE SANER, 2025. [10] P. Preethi, V. Ragavan, C. Abinandhana, G. Umamaheswari, and D. R. Suvethaa, “CodeBlizz: An AI-Powered IDE Plugin for Intelligent Code Assistance,” in Proc. IEEE ICECET, 2024. [11] T. Zhang, V. Kishore, F. Wu, K. Q. Weinberger, and Y. Artzi, “BERTScore: Evaluating Text Generation with BERT,” 2019, arXiv:1904.09675. [12] S. Feuerriegel, J. Hartmann, C. Janiesch, and P. Zschech, “Generative AI,” IEEE Trans. Neural Netw. Learn. Syst., vol. 34, no. 5, pp. 1872–1883, 2023.

Copyright

Copyright © 2026 M. Vigneshwaran, S. Vinith, S. Bahadoorsha Mohamed, G. Gopal. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.