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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM68616
Volume:
10 |
10 |
Oct 2026
Pages:
1-8
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Diploma In Artificial Intelligence & Machine Learning
Zeal Polytechnic
Diploma In Artificial Intelligence & Machine Learning
Zeal Polytechnic
Diploma In Artificial Intelligence & Machine Learning
Zeal Polytechnic
Diploma In Artificial Intelligence & Machine Learning
Zeal Polytechnic
Diploma In Artificial Intelligence & Machine Learning
Zeal Polytechnic
Artificial Intelligence & Machine Learning
Zeal Polytechnic
Software vulnerabilities can introduce serious security risks in applications, making code review an essential part of the software development process. However, manual code inspection can be time-consuming, while conventional automated tools may focus on specific categories of issues and provide fragmented results. This paper proposes an automated code vulnerability reviewing framework based on a role-based multi-agent architecture integrated with GitHub. The proposed system retrieves repository information, identifies supported programming languages, examines project structure and dependencies, and prepares relevant source-code context for analysis. It coordinates three specialized agents: a Security Auditor Agent to identify potential vulnerabilities, a Software Architect Agent to examine code structure and design-related concerns, and a Review Moderator Agent to consolidate findings, remove duplicates, resolve conflicting observations, and prioritize reported issues. Reviews can be initiated automatically through supported GitHub events or manually by developers. The framework also proposes a locally executable environment with a web-based dashboard and command-line interface (CLI), allowing developers to monitor review activities and access structured reports containing issue descriptions, code locations, supporting evidence, and suggested corrections. The system is designed to provide recommendations without automatically modifying source code. This paper presents the proposed architecture, workflow, and evaluation approach; its effectiveness remains subject to future implementation and experimental validation.
KEYWORDS
This paper presented the proposed Automated Code Vulnerability Reviewing framework, which aims to assist developers in reviewing software repositories through a GitHub-integrated, role-based multi-agent architecture. The framework combines repository analysis, source-code context preparation, and specialized review agents to examine security vulnerabilities and software architecture-related concerns. The proposed system consists of three agents: the Security Auditor Agent, the Software Architect Agent, and the Review Moderator Agent. The first two agents perform their respective analyses in parallel, while the moderator consolidates their findings, removes duplicates, handles conflicting observations, and prioritizes the reported issues. The framework also proposes a local execution environment with a web-based dashboard and command-line interface, providing developers with access to structured review reports. Unlike systems that directly modify source code, the proposed framework focuses on identifying potential issues and providing recommendations for developer verification. Its planned integration with GitHub is intended to support both manual and event-triggered code reviews. The paper also discussed the proposed methodology, evaluation approach, limitations, and security considerations. Since the framework is currently at the proposal stage, its practical effectiveness, accuracy, and performance have not yet been experimentally established. Future implementation and testing will be required to validate its capabilities and determine its usefulness in real-world software development environments.
[1] J. Yu, P. Liang, Y. Fu, A. Tahir, M. Shahin, C. Wang, and Y. Cai, “An insight into security code review with LLMs: Capabilities, obstacles and influential factors,” arXiv preprint arXiv:2401.16310, 2024. [Online]. Available: https://arxiv.org/abs/2401.16310 [2] R. I. T. Jensen, V. Tawosi, and S. Alamir, “Software vulnerability and functionality assessment using LLMs,” arXiv preprint arXiv:2403.08429, 2024. [Online]. Available: https://arxiv.org/abs/2403.08429 [3] T. Sharanarthi and S. Polineni, “Multi-Agent LLM Collaboration for Adaptive Code Review, Debugging, and Security Analysis,” in Proc. 2025 International Conference on Mechatronics, Robotics, and Artificial Intelligence (MRAI), 2025, doi: 10.1109/MRAI65197.2025.11135756. [4] V. Belozerov, P. J. Barclay, and A. Sami, “Secure coding with AI—from detection to repair,” Empirical Software Engineering, vol. 31, art. no. 93, 2026, doi: 10.1007/s10664-026-10812-8. [5] Tree-sitter, “Introduction,” Tree-sitter Documentation. [Online]. Available: https://tree-sitter.github.io/tree-sitter/. [Accessed: Oct. 29, 2026]. [6] OWASP Foundation, “OWASP Top 10 for Large Language Model Applications,” OWASP GenAI Security Project, 2026. [Online]. Available: https://genai.owasp.org/llm-top-10/
Publication Date
10/31/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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