LLM-Assisted Repository-Level Generation with Structured Spec-Driven Engineering
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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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 7 of its 34 references that are in the map. First posted on arXiv 4 May 2026.
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7 of 34 references.
References · 7
- 2021Evaluating Large Language Models Trained on Codecited by 8,053
- 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
- 2023Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generationcited by 1,332
- 2023Teaching Large Language Models to Self-Debugcited by 963
- 2023From Copilot to Pilot: Towards AI Supported Software Developmentcited by 14
- 2025Augmenting Large Language Models with Static Code Analysis for Automated Code Quality Improvementscited by 2
- 2025Hierarchical Evaluation of Software Design Capabilities of Large Language Models of Codecited by 1
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Citing papers per year: 2026: 1.
1 paper.