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Code Comprehensor: Hierarchical Documentation and Cross-Modal Retrieval for Large Repositories

Sujoy Roychowdhury, Joseph H. R. Isaac, Jiangning Gao, Martin Klitte, Diarmuid Corcoran · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.30016 · cited by 0 works (OpenAlex) · open-access PDF

Portuguese

Compreender bases de código grandes, complexas e em rápida evolução do contexto do código-padrão permanece um desafio fundamental para a engenharia de software em escala, impedindo o embarque, manutenção, refactoring e análise de impacto. Fornecer essas informações a um Modelo de Linguagem Maior (LLM) é outro obstáculo, já que o LLM deve compreender não apenas o método de código necessário, mas também seus métodos associados no grande esquema do repositório. Proponhamos o Code Comprehensor, um modelo de dupla codificação treinado no estilo CLIP na saída de CodeScribe que combina (i) um callgraph de dependência de código, (ii) o CodeScribe, que usa o código-consciente LLMs para gerar documentação hierárquica em chamadas de repositório, e (

Machine translation (Meta M2M100); the English original follows.

Abstract

Understanding large, complex, and rapidly evolving code bases remains a core challenge for software engineering at scale, hindering onboarding, maintenance, refactoring, and impact analysis. Providing this information to a Large-Language Model (LLM) is another hurdle, as the LLM must understand not only the required code method but also its associated methods in the grand scheme of the repository. We propose Code Comprehensor, a hybrid retrieval-reasoning framework combining (i) a code dependency callgraph, (ii) CodeScribe, which uses code-aware LLMs to generate hierarchical documentation across repository call graphs, and (iii) CodeContrast, a CLIP-style dual-encoder model trained on CodeScribe output to learn aligned text-code embeddings. CodeContrast enables fast, semantically precise retrieval of relevant code-context neighborhoods, which serve as grounded evidence for an MCP-based LLM orchestrator answering domain-specific queries. Evaluation on OpenCV and a proprietary 5M-LOC repository shows that CodeScribe-derived supervision significantly improves cross-modal retrieval in text-to-code accuracy over baseline approaches. Richer textual supervision (hierarchical documentation, parent/sibling context, and LLM-generated synthetic questions) consistently enhances performance across both repositories. LLM-as-a-judge evaluation using the popular RAGAS of the Code Comprehensor output yields above 90% faithfulness scores.

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Topics: Documentation, Computer science, Code (set theory), Information retrieval, Scheme (mathematics), Software

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