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

@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},
      doi = {https://doi.org/10.64388/IREV9I10-1715992}
  }