HackerPapers

arXiv:2303.07839cs.SE

ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design

Jules White, Sam Hays, Quchen Fu, Jesse Spencer-Smith, Douglas C. Schmidt

Early discovery. Linked by one identifiable commenter. This paper is outside the main ranking; a mention is not an endorsement.

Abstract

This paper presents prompt design techniques for software engineering, in the form of patterns, to solve common problems when using large language models (LLMs), such as ChatGPT to automate common software engineering activities, such as ensuring code is decoupled from third-party libraries and simulating a web application API before it is implemented. This paper provides two contributions to research on using LLMs for software engineering. First, it provides a catalog of patterns for software engineering that classifies patterns according to the types of problems they solve. Second, it explores several prompt patterns that have been applied to improve requirements elicitation, rapid prototyping, code quality, refactoring, and system design.

arXiv comments
Journal reference
DOI
Subjects
cs.SESoftware Engineering cs.AIArtificial Intelligence

On Hacker News

Linked by 1 identifiable commenter in 1 comment across 1 thread, from Mar 2023 to Mar 2023.

Comments linking this paper, per year.

Discussed in comments1 thread

  1. Testing GPT 4's code-writing capabilities with some real world problems 1 comment

    • I run into this a lot myself. In our paper (https://arxiv.org/pdf/2303.07839.pdf) we specify a 'Specification Disambiguation' pattern that attempts to address this very thing.

      gsamuelhaysView on HNParent context

Submitted to Hacker News

Connected through shared HN threads, not inferred similarity or a recommendation.