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

arXiv:2605.0245553.7
AI Analysis

This work addresses the challenge of scaling LLM code generation to repository-level systems, offering a paradigm that improves verifiability and quality for software engineers.

The paper proposes structured spec-driven engineering (SSDE) to improve repository-level code generation with LLMs, showing in a pilot study that structured specifications enhance output quality and verifiability across three MVC systems using five LLMs.

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.

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