# From Requirements to Code: Understanding Developer Practices in LLM-Assisted Software Engineering

arXiv: https://arxiv.org/abs/2507.07548 · HTML: https://mlsanity.com/paper/2507.07548/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2025.

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

- [TraceDev: A Traceability-Driven Multi-agent Framework for Requirement-to-Code Development](https://mlsanity.com/paper/2607.18886/) (2026)
- [Reward-Free Code Alignment from Pretrained or Fine-Tuned LLM: Unpacking the Trade-offs for Code Generation](https://mlsanity.com/paper/2606.28998/) (2026)
- [How Well Can AI Generate Backlogs from App Mockups?](https://mlsanity.com/paper/2607.22902/) (2026)
- [Don't Guess, Just Ask: Evaluating Interactive Requirement Clarification for Repository-Level Software Development](https://mlsanity.com/paper/2610.13022/) (2026)

## Abstract (arXiv)

With the advent of generative LLMs and their advanced code generation capabilities, some people already envision the end of traditional software engineering, as LLMs may be able to produce high-quality code based solely on the requirements a domain expert feeds into the system. The feasibility of this vision can be assessed by understanding how developers currently incorporate requirements when using LLMs for code generation-a topic that remains largely unexplored. We interviewed 18 practitioners from 14 companies to understand how they (re)use information from requirements and other design artifacts to feed LLMs when generating code. Based on our findings, we propose a theory that explains the processes developers employ and the artifacts they rely on. Our theory suggests that requirements, as typically documented, are too abstract for direct input into LLMs. Instead, they must first be manually decomposed into programming tasks, which are then enriched with design decisions and architectural constraints before being used in prompts. Our study highlights that fundamental RE work is still necessary when LLMs are used to generate code. Our theory is important for contextualizing scientific approaches to automating requirements-centric SE tasks.