# Requirements are All You Need: From Requirements to Code with LLMs

arXiv: https://arxiv.org/abs/2406.10101 · HTML: https://mlsanity.com/paper/2406.10101/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2024.

## Built on by (most cited first, 22 of 22)

- [From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future](https://mlsanity.com/paper/2408.02479/) (2024)
- [Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AI](https://mlsanity.com/paper/2505.19443/) (2025)
- [Exploring Autonomous Agents: A Closer Look at Why They Fail When Completing Tasks](https://mlsanity.com/paper/2508.13143/) (2025)
- [Generative AI for Software Architecture. Applications, Challenges, and Future Directions](https://mlsanity.com/paper/2503.13310/) (2025)
- [Generative AI for Requirements Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2409.06741/) (2024)
- [Collaboration Dynamics and Reliability Challenges of Multi-Agent LLM Systems in Finite Element Analysis](https://mlsanity.com/paper/2408.13406/) (2024)
- [Requirements Development and Formalization for Reliable Code Generation: A Multi-Agent Vision](https://mlsanity.com/paper/2508.18675/) (2025)
- [Mitigating Gender Bias in Code Large Language Models via Model Editing](https://mlsanity.com/paper/2410.07820/) (2024)
- [Assessing LLMs for Front-end Software Architecture Knowledge](https://mlsanity.com/paper/2502.19518/) (2025)
- [Can LLMs Generate User Stories and Assess Their Quality?](https://mlsanity.com/paper/2507.15157/) (2025)
- [The State of Generative AI in Software Development: Insights from Literature and a Developer Survey](https://mlsanity.com/paper/2603.16975/) (2026)
- [Goal2Story: A Multi-Agent Fleet based on Privately Enabled sLLMs for Impacting Mapping on Requirements Elicitation](https://mlsanity.com/paper/2503.13279/) (2025)
- [A Blueprint for AI-Driven Software Quality: Integrating LLMs with Established Standards](https://mlsanity.com/paper/2505.13766/) (2025)
- [Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report](https://mlsanity.com/paper/2608.11965/) (2026)
- [Improving Performance of Commercially Available AI Products in a Multi-Agent Configuration](https://mlsanity.com/paper/2410.22129/) (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)
- [Application Modernization with LLMs: Addressing Core Challenges in Reliability, Security, and Quality](https://mlsanity.com/paper/2506.10984/) (2025)
- [The Software Engineering Simulations Lab: Agentic AI for RE Quality Simulations](https://mlsanity.com/paper/2511.17762/) (2025)
- [HanoiWorld : A Joint Embedding Predictive Architecture BasedWorld Model for Autonomous Vehicle Controller](https://mlsanity.com/paper/2601.01577/) (2026)
- [Exploring LLMs for User Story Extraction from Mockups](https://mlsanity.com/paper/2602.16997/) (2026)
- [Integrating AI into Requirements Quality Learning in Software Engineering Education: A TPACK-Guided Empirical Study](https://mlsanity.com/paper/2607.28176/) (2026)
- [Don't Guess, Just Ask: Evaluating Interactive Requirement Clarification for Repository-Level Software Development](https://mlsanity.com/paper/2610.13022/) (2026)

## Abstract (arXiv)

The pervasive use of textual formats in the documentation of software requirements presents a great opportunity for applying large language models (LLMs) to software engineering tasks. High-quality software requirements not only enhance the manual software development process but also position organizations to fully harness the potential of the emerging LLMs technology. This paper introduces a tailored LLM for automating the generation of code snippets from well-structured requirements documents. This LLM is augmented with knowledge, heuristics, and instructions that are pertinent to the software development process, requirements analysis, object-oriented design, and test-driven development, effectively emulating the expertise of a seasoned software engineer. We introduce a "Progressive Prompting" method that allows software engineers to engage with this LLM in a stepwise manner. Through this approach, the LLM incrementally tackles software development tasks by interpreting the provided requirements to extract functional requirements, using these to create object-oriented models, and subsequently generating unit tests and code based on the object-oriented designs. We demonstrate the LLM's proficiency in comprehending intricate user requirements and producing robust design and code solutions through a case study focused on the development of a web project. This study underscores the potential of integrating LLMs into the software development workflow to significantly enhance both efficiency and quality. The tailored LLM is available at https://chat.openai.com/g/g-bahoiKzkB-software-engineer-gpt.