A Comparative Study of Code Generation using ChatGPT 3.5 across 10 Programming Languages

Alessio Buscemi
8/8/2023
cs.SEcs.AI

No confident code implementation found for this paper. 1 possible match can be shown below.

Abstract

Large Language Models (LLMs) are advanced Artificial Intelligence (AI) systems that have undergone extensive training using large datasets in order to understand and produce language that closely resembles that of humans. These models have reached a level of proficiency where they are capable of successfully completing university exams across several disciplines and generating functional code to handle novel problems. This research investigates the coding proficiency of ChatGPT 3.5, a LLM released by OpenAI in November 2022, which has gained significant recognition for its impressive text generating and code creation capabilities. The skill of the model in creating code snippets is evaluated across 10 various programming languages and 4 different software domains. Based on the findings derived from this research, major unexpected behaviors and limitations of the model have been identified. This study aims to identify potential areas for development and examine the ramifications of automated code generation on the evolution of programming languages and on the tech industry.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 1 weaker match is hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Cite this paper

@article{buscemi2023comparative,
  title  = {A Comparative Study of Code Generation using ChatGPT 3.5 across 10 Programming Languages},
  author = {Alessio Buscemi},
  year   = {2023},
  eprint = {2308.04477},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2308.04477v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2308.04477.svg)](https://codeofpaper.com/paper/2308.04477)

Discussion