GPT in Code Conversion: Achieving Agile, Accurate, and Effective Translations Across Programming Languages

Year : 2024 | Volume : 11 | Issue : 02 | Page : 11 20
    By

    Prashant D. Sawant,

  1. A.I. Research Director, AI-D Consultancy, Melbourne, Australia

Abstract

This comprehensive study meticulously investigates the intricacies and challenges inherent in the process of code conversion across a diverse range of programming languages. It provides an in-depth examination of select instances where code conversion poses significant challenges, and thoughtfully proposes innovative solutions to these complex problems. A key highlight of this research is the use of Generative Pre-trained Transformers (GPTs) like Copilot, which have proven to be instrumental in achieving fast, accurate, and effective code conversions. The study demonstrates how GPTs have solved difficult code conversions with a few intricate examples. The research further delves into the broader implications of these solutions, assessing their impact on the field of programming and software development. Additionally, the study identifies and discusses potential areas for future research, paving the way for further advancements in this critical aspect of programming. This research serves as a valuable resource for both novice and experienced developers navigating the complexities of code conversion.

Keywords: Generative Pre-trained Transformers (GPTs), code conversion, software development, programming languages, copilot, artificial intelligence

[This article belongs to Journal of Artificial Intelligence Research & Advances ]

How to cite this article:
Prashant D. Sawant. GPT in Code Conversion: Achieving Agile, Accurate, and Effective Translations Across Programming Languages. Journal of Artificial Intelligence Research & Advances. 2024; 11(02):11-20.
How to cite this URL:
Prashant D. Sawant. GPT in Code Conversion: Achieving Agile, Accurate, and Effective Translations Across Programming Languages. Journal of Artificial Intelligence Research & Advances. 2024; 11(02):11-20. Available from: https://journals.stmjournals.com/joaira/article=2024/view=155821


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Regular Issue Subscription Original Research
Volume 11
Issue 02
Received 23/04/2024
Accepted 30/04/2024
Published 20/06/2024
352

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