# LLM-Assisted Repository-Level Generation with Structured Spec-Driven Engineering

arXiv: https://arxiv.org/abs/2605.02455 · HTML: https://mlsanity.com/paper/2605.02455/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 34 references that are in the map.
- First posted on arXiv 4 May 2026.

## Builds on (most cited first, 7 of 7)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation](https://mlsanity.com/paper/2305.01210/) (2023)
- [Teaching Large Language Models to Self-Debug](https://mlsanity.com/paper/2304.05128/) (2023)
- [From Copilot to Pilot: Towards AI Supported Software Development](https://mlsanity.com/paper/2303.04142/) (2023)
- [Augmenting Large Language Models with Static Code Analysis for Automated Code Quality Improvements](https://mlsanity.com/paper/2506.10330/) (2025)
- [Hierarchical Evaluation of Software Design Capabilities of Large Language Models of Code](https://mlsanity.com/paper/2511.20933/) (2025)

## Built on by (most cited first, 1 of 1)

- [How Do Practitioners Build SE Agents? Insights from a Mixed-Methods Study](https://mlsanity.com/paper/2607.10856/) (2026)

## Abstract (arXiv)

State-of-the-art Large Language Models (LLMs) excel in code generation at the function level. However, the output quality significantly declines when scaling to repository-level systems. Current workflows relying only on natural language prompts suffer from inherent ambiguity and a lack of verifiability. To address this, we propose structured spec-driven engineering (SSDE), a paradigm that leverages structured artifacts to guide LLM generation. We argue that structured specifications as LLM inputs make high-quality, repository-level code generation a tangible goal, while at the same time offering superior verifiability, leading to significant potential for improvement. We first investigate the feasibility of this vision through a pilot study generating Model-View-Controller (MVC) business logic for three software systems using five LLMs, and then highlight the potential, challenges, and future roadmap for SSDE.