Exploring Prompt Patterns in AI-Assisted Code Generation: Towards Faster and More Effective Developer-AI Collaboration

TL;DR

This paper investigates structured prompt patterns in AI-assisted code generation to reduce interaction cycles, improve efficiency, and enhance code quality in developer-AI collaboration.

Contribution

It introduces and evaluates seven prompt patterns using the DevGPT dataset, identifying effective strategies for minimizing interactions and improving code generation quality.

Findings

Patterns like 'Context and Instruction' significantly reduce iterations.

Prompt engineering can streamline developer-AI collaboration.

Effective prompts balance clarity, precision, and efficiency.

Abstract

The growing integration of AI tools in software development, particularly Large Language Models (LLMs) such as ChatGPT, has revolutionized how developers approach coding tasks. However, achieving high-quality code often requires iterative interactions, which can be time-consuming and inefficient. This paper explores the application of structured prompt patterns to minimize the number of interactions required for satisfactory AI-assisted code generation. Using the DevGPT dataset, we analyzed seven distinct prompt patterns to evaluate their effectiveness in reducing back-and-forth communication between developers and AI. Our findings highlight patterns such as ''Context and Instruction'' and ''Recipe'' as particularly effective in achieving high-quality outputs with minimal iterations. The study emphasizes the potential for prompt engineering to streamline developer-AI collaboration, providing practical insights into crafting prompts that balance precision, efficiency, and clarity.