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

**Authors:** Jules White, Sam Hays, Quchen Fu, Jesse Spencer-Smith, Douglas C. Schmidt  
**arXiv:** [2303.07839](https://arxiv.org/abs/2303.07839)  
**Date:** 2023-03-11  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2303.07839v1/

## Key points

- The paper introduces prompt design techniques for software engineering using large language models (LLMs) like ChatGPT.
- The goal is to address common problems encountered when utilizing LLMs in automating software engineering tasks.
- Tasks include ensuring code decoupling from third-party libraries and simulating a web application API before implementation.
- The paper presents a comprehensive catalog of patterns that classify various software engineering problems they solve.
- This catalog serves as a valuable resource for practitioners seeking effective solutions to specific challenges.
- The paper explores several prompt patterns successfully applied in different areas of software engineering, such as requirements elicitation, rapid prototyping, code quality improvement, refactoring, and system design enhancement.
- By employing these prompt patterns, practitioners can leverage the power of LLMs to streamline their development processes and achieve higher efficiency.
- The authors of the paper are Jules White, Sam Hays, Quchen Fu, Jesse Spencer-Smith, and Douglas C. Schmidt.
- Their research provides practical guidance through pattern-based approaches on how LLMs can be effectively utilized in software engineering.
- The paper offers valuable insights into the use of LLMs like ChatGPT for automating software engineering activities.

## Summary

This paper, titled "ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design," introduces prompt design techniques for software engineering using large language models (LLMs) like ChatGPT. The goal is to address common problems encountered when utilizing LLMs in automating software engineering tasks. These tasks include ensuring code decoupling from third-party libraries and simulating a web application API before implementation. The paper makes two significant contributions to the field of LLMs in software engineering. Firstly, it presents a comprehensive catalog of patterns that classify various software engineering problems they solve. This catalog serves as a valuable resource for practitioners seeking effective solutions to specific challenges. Secondly, the paper explores several prompt patterns that have been successfully applied in different areas of software engineering. These areas include requirements elicitation, rapid prototyping, code quality improvement, refactoring, and system design enhancement. By employing these prompt patterns, practitioners can leverage the power of LLMs to streamline their development processes and achieve higher efficiency. The authors of this paper are Jules White, Sam Hays, Quchen Fu, Jesse Spencer-Smith, and Douglas C. Schmidt. Their research sheds light on how LLMs can be effectively utilized in software engineering by providing practical guidance through pattern-based approaches. In conclusion, this paper offers valuable insights into the use of LLMs like ChatGPT for automating software engineering activities. It provides a comprehensive catalog of patterns and explores various prompt patterns that have proven effective in improving code quality, refactoring processes, requirements elicitation techniques, and overall software design.