Automatic Programming: Large Language Models and Beyond

Michael R. Lyu, Baishakhi Ray, Abhik Roychoudhury, Shin Hwei Tan, Patanamon Thongtanunam
5/3/2024
cs.SEcs.AIcs.LG

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

Abstract

Automatic programming has seen increasing popularity due to the emergence of tools like GitHub Copilot which rely on Large Language Models (LLMs). At the same time, automatically generated code faces challenges during deployment due to concerns around quality and trust. In this article, we study automated coding in a general sense and study the concerns around code quality, security and related issues of programmer responsibility. These are key issues for organizations while deciding on the usage of automatically generated code. We discuss how advances in software engineering such as program repair and analysis can enable automatic programming. We conclude with a forward looking view, focusing on the programming environment of the near future, where programmers may need to switch to different roles to fully utilize the power of automatic programming. Automated repair of automatically generated programs from LLMs, can help produce higher assurance code from LLMs, along with evidence of assurance

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{lyu2024automatic,
  title  = {Automatic Programming: Large Language Models and Beyond},
  author = {Michael R. Lyu and Baishakhi Ray and Abhik Roychoudhury and Shin Hwei Tan and Patanamon Thongtanunam},
  year   = {2024},
  eprint = {2405.02213},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2405.02213v2}
}

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/2405.02213.svg)](https://codeofpaper.com/paper/2405.02213)

Discussion