Design and Implementation of a MERN-Based AI Code Generation and Browser-Sandboxed Execution Platform Using Large Language Models

• Author(s): Aryan Gawade; Tushar Chawre; Rushabh Gaur; Om Dhanke; Dr. Niranjan Kulkarni
• Paper ID: 1715992
• Page: 362-369
• Published Date: 07-04-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 10 April-2026
• DOI: https://doi.org/10.64388/IREV9I10-1715992

Abstract

The integration of Large Language Models (LLMs) into software development environments has significantly enhanced developer productivity through intelligent code generation. However, securely and efficiently executing AI-generated code remains a challenge due to potential security risks and infrastructure overhead. This paper presents the design and implementation of a MERN-based web application that integrates the Gemini LLM for multi-language code generation and utilizes browser-based Web Containers for secure sandboxed execution. The proposed system eliminates the need for local setup and server-side execution by leveraging client-side isolated runtime environments. Performance evaluation demonstrates reduced backend load and reliable multi-user execution.

Keywords

Large Language Models, AI Code Generation, MERN Stack, Web Containers, Sandboxed Execution, BrowserBased IDE

Citations

IRE Journals:
Aryan Gawade, Tushar Chawre, Rushabh Gaur, Om Dhanke, Dr. Niranjan Kulkarni "Design and Implementation of a MERN-Based AI Code Generation and Browser-Sandboxed Execution Platform Using Large Language Models" Iconic Research And Engineering Journals Volume 9 Issue 10 2026 Page 362-369 https://doi.org/10.64388/IREV9I10-1715992

IEEE:
Aryan Gawade, Tushar Chawre, Rushabh Gaur, Om Dhanke, Dr. Niranjan Kulkarni "Design and Implementation of a MERN-Based AI Code Generation and Browser-Sandboxed Execution Platform Using Large Language Models" Iconic Research And Engineering Journals, vol. 9, no. 10, Apr. 2026, doi: https://doi.org/10.64388/IREV9I10-1715992

APA:
Aryan Gawade, Tushar Chawre, Rushabh Gaur, Om Dhanke, Dr. Niranjan Kulkarni (2026). Design and Implementation of a MERN-Based AI Code Generation and Browser-Sandboxed Execution Platform Using Large Language Models. Iconic Research And Engineering Journals, 9(10). doi: https://doi.org/10.64388/IREV9I10-1715992

MLA:
Aryan Gawade, Tushar Chawre, Rushabh Gaur, Om Dhanke, Dr. Niranjan Kulkarni "Design and Implementation of a MERN-Based AI Code Generation and Browser-Sandboxed Execution Platform Using Large Language Models" Iconic Research And Engineering Journals, vol. 9, no. 10, Apr. 2026. Crossref, https://doi.org/10.64388/IREV9I10-1715992

BibTeX

@article{1715992,
author = {Aryan Gawade, Tushar Chawre, Rushabh Gaur, Om Dhanke, Dr. Niranjan Kulkarni},
title = {Design and Implementation of a MERN-Based AI Code Generation and Browser-Sandboxed Execution Platform Using Large Language Models},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {10},
pages = {362-369},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1715992.pdf},
abstract = {The integration of Large Language Models (LLMs) into software development environments has significantly enhanced developer productivity through intelligent code generation. However, securely and efficiently executing AI-generated code remains a challenge due to potential security risks and infrastructure overhead. This paper presents the design and implementation of a MERN-based web application that integrates the Gemini LLM for multi-language code generation and utilizes browser-based Web Containers for secure sandboxed execution. The proposed system eliminates the need for local setup and server-side execution by leveraging client-side isolated runtime environments. Performance evaluation demonstrates reduced backend load and reliable multi-user execution.},
keywords = {Large Language Models, AI Code Generation, MERN Stack, Web Containers, Sandboxed Execution, BrowserBased IDE},
month = {April}
}