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

March 11, 2023 Β· Declared Dead Β· πŸ› arXiv.org

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Authors Jules White, Sam Hays, Quchen Fu, Jesse Spencer-Smith, Douglas C. Schmidt arXiv ID 2303.07839 Category cs.SE: Software Engineering Cross-listed cs.AI Citations 231 Venue arXiv.org Last Checked 3 months ago
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.
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