Context-as-a-Service: Surfacing Cross-File Dependency Chains for LLM-Generated Developer Documentation

2026-06-04 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Expert, extended

Summary

Context-as-a-Service (CaaS) is a retrieval layer that LLM agents query to find evidence across the codebase as they review or generate documentation. CaaS indexes source code, API references, and upstream documentation, enabling agents to query the index through tool calls combining keyword and semantic search. Evaluated using Claude Sonnet 4.6 on a production SDK with approximately 200 source files, CaaS-augmented agents surfaced 8 additional findings (2 cross-file factual errors, 2 underspecified API comments, 1 executable bug, 1 API-usage improvement, and 2 missing prerequisites) that baseline agents with ordinary repository tools missed. These findings required tracing non-obvious dependency chains across various file types. Furthermore, adding CaaS reduced wall-clock time by 22% to 34% across two tasks and lowered input-token usage over five runs per condition.

Key takeaway

For AI Engineers or ML Scientists developing LLM agents for code documentation, integrating a retrieval layer like CaaS is crucial. Your agents can generate locally plausible but globally incorrect documentation without it, missing critical cross-file dependencies. Implement a retrieval-augmented generation (RAG) system to index diverse codebase elements, enabling agents to proactively identify and correct subtle errors, thereby improving documentation accuracy and reducing review time.

Key insights

CaaS enhances LLM documentation agents by surfacing non-obvious cross-file dependencies, improving accuracy and efficiency.

Principles

• Documentation claims often depend on behavior distributed across files.
• Locally plausible documentation can be globally incorrect.
• Retrieval augments an agent's exploration, it does not replace file inspection.

Method

CaaS employs a four-stage pipeline: ingestion (source code, API references, upstream documentation), storage (BM25 and DRAMA indexing), retrieval (tool-callable interface combining BM25 and DRAMA results), and a review layer for labeling findings.

In practice

• Index diverse source types: code, API references, tests, examples.
• Combine keyword and dense search for robust retrieval.
• Guide agent exploration with compact, pre-ranked snippets.

Topics

• LLM Agents
• Developer Documentation
• Retrieval-Augmented Generation
• Cross-File Dependencies
• Codebase Indexing
• Semantic Search

Best for: AI Architect, Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

Related on AIssential

• I Spent 3 Hours Building Something That Would Have Taken My Team 3 Weeks. It’s Called RAG. — LLMs are limited to their training data, necessitating RAG for accurate, context-specific responses to novel queries.
• Automated Root-Cause Subclassification and No-Code Fix Generation for Invalid Bug Reports — Automating invalid bug report subclassification and no-code fix generation significantly reduces software maintenance overhead.
• Multimodal RAG & Agentic Workflows via Enterprise h2oGPTe | Part 15 — RAG effectively grounds LLMs in enterprise knowledge by integrating diverse data sources and advanced retrieval mechanisms.
• Diagnosing Knowledge Gaps in LLM Tool Use: An Agentic Benchmark for Novel API Acquisition — LLM tool use with novel APIs requires specific knowledge components, with usage examples being critical, and retrieval/tuning playing complementary roles.
• Grounding Your LLM: A Practical Guide to RAG for Enterprise Knowledge Bases — RAG systems combine LLMs with dynamic knowledge retrieval to provide accurate, auditable, and updatable answers for enterprise data.
• Retrieval-Augmented Generation — A Deep Dive into Components — RAG improves LLM accuracy and relevance by integrating external knowledge retrieval with text generation.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.