# Prioritizing Software Requirements Using Large Language Models

arXiv: https://arxiv.org/abs/2405.01564 · HTML: https://mlsanity.com/paper/2405.01564/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2024.

## Built on by (most cited first, 4 of 4)

- [Experimenting with Multi-Agent Software Development: Towards a Unified Platform](https://mlsanity.com/paper/2406.05381/) (2024)
- [Leveraging Graph-RAG and Prompt Engineering to Enhance LLM-Based Automated Requirement Traceability and Compliance Checks](https://mlsanity.com/paper/2412.08593/) (2024)
- [LLM assisted web application functional requirements generation: A case study of four popular LLMs over a Mess Management System](https://mlsanity.com/paper/2505.18019/) (2025)
- [Are We Aligned? A Preliminary Investigation of the Alignment of Responsible AI Values between LLMs and Human Judgment](https://mlsanity.com/paper/2511.04157/) (2025)

## Abstract (arXiv)

Large Language Models (LLMs) are revolutionizing Software Engineering (SE) by introducing innovative methods for tasks such as collecting requirements, designing software, generating code, and creating test cases, among others. This article focuses on requirements engineering, typically seen as the initial phase of software development that involves multiple system stakeholders. Despite its key role, the challenge of identifying requirements and satisfying all stakeholders within time and budget constraints remains significant. To address the challenges in requirements engineering, this study introduces a web-based software tool utilizing AI agents and prompt engineering to automate task prioritization and apply diverse prioritization techniques, aimed at enhancing project management within the agile framework. This approach seeks to transform the prioritization of agile requirements, tackling the substantial challenge of meeting stakeholder needs within set time and budget limits. Furthermore, the source code of our developed prototype is available on GitHub, allowing for further experimentation and prioritization of requirements, facilitating research and practical application.