Integration Of AI Model For Code Generation And Correction

Abstract

This research explores integrating a powerful AI language model into a web-based coding assistance Chabot. The proposed system features a user-friendly frontend with a prompt submission interface, allowing developers to input coding queries, requirements, or snippets in natural language. Upon submission, the request is processed by the backend, which integrates the AI model's API to generate relevant code, explanations, or solutions based on the user's input.

We present the system's architecture, detailing the frontend's intuitive design and the backbend’s seamless API integration. Implementation specifics, including technologies employed, data processing techniques, and the communication workflow between components, are outlined. To evaluate performance, we conduct experiments across diverse coding tasks and languages, analyzing results using quantitative metrics like BLEU scores, CodeBLEU, and qualitative human assessments of accuracy, completeness, and usability.

Our findings highlight the AI model's strengths in comprehending natural language queries and providing pertinent code snippets and elucidations. However, challenges persist in generating semantically sound code for intricate tasks and niche domains. We address ethical considerations, responsible AI practices, potential biases, and the necessity of human oversight in deploying such AI-driven coding aids.

Finally, we outline future research avenues, including integrating program analysis techniques, domain-specific knowledge, and exploring multi-modal approaches combining natural language and visual programming interfaces to enhance the semantic correctness of generated code.