REprompt: Prompt Generation for Intelligent Software Development Guided by Requirements Engineering

TL;DR

REprompt is a framework that automates prompt generation for AI-driven software development by integrating requirements engineering principles, improving the alignment of prompts with formal user requirements.

Contribution

It introduces a multi-agent prompt optimization framework that incorporates requirements engineering to enhance prompt effectiveness in software development tasks.

Findings

REprompt effectively optimizes prompts grounded in requirements engineering.

The framework improves alignment with formal requirements.

Experimental results show enhanced prompt quality and relevance.

Abstract

The rapid development of large language models is transforming software development. Beyond serving as code auto-completion tools in integrated development environments, large language models increasingly function as foundation models within coding agents in vibe-coding scenarios. In such settings, prompts play a central role in agent-based intelligent software development, as they not only guide the behavior of large language models but also serve as carriers of user requirements. Under the dominant conversational paradigm, prompts are typically divided into system prompts and user prompts. System prompts provide high-level instructions to steer model behavior and establish conversational context, while user prompts represent inputs and requirements provided by human users. Despite their importance, designing effective prompts remains challenging, as it requires expertise in both prompt engineering and software engineering, particularly requirements engineering. To reduce the burden of manual prompt construction, numerous automated prompt engineering methods have been proposed. However, most existing approaches neglect the methodological principles of requirements engineering, limiting their ability to generate artifacts that conform to formal requirement specifications in realistic software development scenarios. To address this gap, we propose REprompt, a multi-agent prompt optimization framework guided by requirements engineering. Experiment results demonstrate that REprompt effectively optimizes both system and user prompts by grounding prompt generation in requirements engineering principles.