mlsanityData to 7 Oct 2026

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

arXiv 2605.02455 ↗First posted 4 May 2026cs.SE cs.AIcited by 1

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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

  1. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  2. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  3. 2023Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generationcited by 1,332
  4. 2023Teaching Large Language Models to Self-Debugcited by 963
  5. 2023From Copilot to Pilot: Towards AI Supported Software Developmentcited by 14
  6. 2025Augmenting Large Language Models with Static Code Analysis for Automated Code Quality Improvementscited by 2
  7. 2025Hierarchical Evaluation of Software Design Capabilities of Large Language Models of Codecited by 1

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