GDT: Generate, Debug, and Translate Code
This paper introduces the design and development of an AI-powered web platform that integrates Generative AI with the MERN stack (MongoDB, Express, React, Node.js) to streamline essential coding tasks for software developers. The platform combines multiple functionalities aimed at improving coding efficiency and assisting developers throughout the software development lifecycle. The first key feature, Code Generation, enables users to input problem descriptions or project requirements in natural language and generates corresponding code automatically, saving developers time in writing boilerplate or complex code structures. The second feature, Code Debugging, allows users to upload code with errors, after which the system identifies the error locations, provides detailed explanations, and suggests corrected, error-free code. The third feature, Code Translation, offers the ability to translate code from one programming language to another, facilitating multi-language development without the need for manual conversion. Additionally, the platform includes Trusted Company Reviews, which provide insights into company-specific experiences from past developers, helping users make informed career and workplace decisions. By integrating OpenAI's GPT model, the platform leverages advanced natural language processing (NLP) techniques to accurately interpret user inputs and generate relevant outputs across the various modules. The use of the MERN stack ensures a highly responsive and scalable architecture, with MongoDB managing data storage, Node.js and Express handling backend operations, and React providing a dynamic user interface. This platform is designed to cater to both novice and experienced developers, reducing the complexity and time associated with manual code writing, debugging, and language translation. As a comprehensive tool, it aims to accelerate the development process, foster productivity, and assist developers in overcoming coding challenges effectively.
Authors
- R. Srimathi
- Archana Pown T
- Marzooqa Naeema Kather
- Vazhan Arul Santhiya R
- Pappathi Jancy Rani M
Publication Details
- Published
- 2025-02-21
- DOI
- https://doi.org/10.1109/icitiit64777.2025.11041511
- Primary Topic
- Mathematics, Computing, and Information Processing
- Type
- article
- Field-Weighted Citation Impact
- 0.00