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

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arxiv 2406.10101 v2 pith:RHJRRZZS submitted 2024-06-14 cs.SE

classification cs.SE
keywords requirementssoftwaredevelopmentcodellmsobject-orienteddesignenhance
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. An Iterative Test-and-Repair Framework for Competitive Code Generation

    cs.SE 2026-04 unverdicted novelty 7.0 of 10

    FixAudit improves LLM code generation on competitive programming benchmarks by training a shared model for iterative code-aware test generation and repair, achieving 35%+ gains in Pass@1 over baselines on the same 7B model.

  2. Aligning Requirement for Large Language Model's Code Generation

    cs.SE 2025-09 conditional novelty 5.0 of 10

    An LLM-based specification-alignment method that lifts the model's perceived requirements from generated code and applies ten alignment rules improves Pass@1 by about 30% over existing baselines.

  3. Can LLMs Generate User Stories and Assess Their Quality?

    cs.SE 2025-07 conditional novelty 4.0 of 10

    LLMs generate user stories with human-like coverage and style but lower diversity, and they assess semantic quality well when given explicit codebook criteria, though human oversight is still needed.

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