mlsanityData to 7 Oct 2026

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

arXiv 2406.10101 ↗First posted 14 Jun 2024cs.SEcited by 21

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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.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Jun 2024.

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Citing papers per year: 2024: 5; 2025: 11; 2026: 5.

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  1. 2024From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Futurecited by 85
  2. 2025Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AIcited by 39
  3. 2025Exploring Autonomous Agents: A Closer Look at Why They Fail When Completing Taskscited by 20
  4. 2025Generative AI for Software Architecture. Applications, Challenges, and Future Directionscited by 15
  5. 2024Generative AI for Requirements Engineering: A Systematic Literature Reviewcited by 10
  6. 2024Collaboration Dynamics and Reliability Challenges of Multi-Agent LLM Systems in Finite Element Analysiscited by 6
  7. 2024Mitigating Gender Bias in Code Large Language Models via Model Editingcited by 3
  8. 2025Requirements Development and Formalization for Reliable Code Generation: A Multi-Agent Visioncited by 3
  9. 2025Assessing LLMs for Front-end Software Architecture Knowledgecited by 2
  10. 2025Can LLMs Generate User Stories and Assess Their Quality?cited by 2
  11. 2026The State of Generative AI in Software Development: Insights from Literature and a Developer Surveycited by 2
  12. 2025Goal2Story: A Multi-Agent Fleet based on Privately Enabled sLLMs for Impacting Mapping on Requirements Elicitationcited by 1
9 more
  1. 2025A Blueprint for AI-Driven Software Quality: Integrating LLMs with Established Standardscited by 1
  2. 2026Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Reportcited by 1
  3. 2024Improving Performance of Commercially Available AI Products in a Multi-Agent Configurationcited by 0
  4. 2025LLM assisted web application functional requirements generation: A case study of four popular LLMs over a Mess Management Systemcited by 0
  5. 2025Application Modernization with LLMs: Addressing Core Challenges in Reliability, Security, and Qualitycited by 0
  6. 2025The Software Engineering Simulations Lab: Agentic AI for RE Quality Simulationscited by 0
  7. 2026HanoiWorld : A Joint Embedding Predictive Architecture BasedWorld Model for Autonomous Vehicle Controllercited by 0
  8. 2026Exploring LLMs for User Story Extraction from Mockupscited by 0
  9. 2026Integrating AI into Requirements Quality Learning in Software Engineering Education: A TPACK-Guided Empirical Studycited by 0