Graph rAG improves code migration by preserving structure and dependencies
Beyond Vector Similarity: Hierarchical Context-Aware Graph RAG vs Standard RAG in Enterprise Code Migration
Artificial Intelligence
Summary
Modernizing old software into smaller, manageable parts is hard, especially when automatically translating code with AI. The authors show that the usual method which treats code pieces separately misses important connections, causing many errors. They introduce a new approach that understands the code's structure better by using graphs to keep track of these connections. This new method makes the translated code more reliable, although sometimes it creates slightly more complex code than before.
What this means in practice
- •For enterprise software engineers: Use the graph-based RAG method to reduce errors while automatically translating large legacy codebases into microservices architectures.
- •For cloud migration teams: Improve automated code translation tools by integrating hierarchical context graphs to maintain architectural dependencies during cloud migrations.
Authors
Nilesh Jaiswal, Aniket Agrawal, Arjit Shukla, Divya Malhotra, Saurabh Garg, Suchit Puri, Suddhasatwa Bhaumik
Abstract
As enterprises modernize legacy monolithic systems to microservices, Large Language Models (LLMs) are heavily utilized for automated code translation. However, traditional vector-based Retrieval-Augmented Generation (Standard RAG) struggles to capture topological relationships. It fetches isolated chunks that sever inheritance chains, leading to high compilation failure rates. This paper introduces a Hierarchical Context-Resident Graph (HCRG) methodology to resolve these limitations. Our pipeline uses tree-sitter for Abstract Syntax Tree (AST) extraction, maps architectural edges into a Google Cloud Spanner Property Graph, and serializes this structure into a Gemini Context Cache for topological, parent-first code translation. We shift evaluation from naive text-overlap to a custom 7-metric Software Engineering framework. Traditional metrics like CodeBLEU (which scored 91% for both methods) effectively masked Standard RAG's structural failures behind syntactically plausible but broken code. Empirically, Graph RAG decisively mitigates dependency loss: API hallucination rates dropped from 56.4% to 16.2%, Dependency Resolution Quality improved from 34.8% to 65.9%, and Parent-Child Consistency rose from 26.7% to 45.5%. However, Graph RAG introduces specific trade-offs. The dense global context causes defensive over-engineering by the LLM, reducing Cyclomatic Complexity Consistency from 71.6% to 46.7%, and slightly degrades Docstring Preservation (67.0% to 61.0%). Ultimately, while trading code complexity for reduced hallucinations, Graph RAG provides a substantially more viable, architecturally sound path for automated enterprise codebase modernization.