Most AI coding tools fail at large-scale refactoring because they lack a structural understanding of the code's "gravity."
I've been experimenting with applying the Label Propagation Algorithm (LPA) to AST dependency graphs. By treating variables and functions as nodes, we can identify communities (clusters) within a single file.
I integrated this into a tool for Claude Code called VHO. It helps dismantle monolithic React/Vue hooks by grounded mathematical analysis rather than just LLM prediction.
Technical breakdown:
AST Parsing: To build the reference graph.
LPA: To find the optimal boundaries for extraction.
Agentic Execution: Using Claude Code to perform the actual file I/O and refactoring.
Is graph-theory-assisted refactoring the future of AI coding? I'd love to discuss the methodology.
I've been experimenting with applying the Label Propagation Algorithm (LPA) to AST dependency graphs. By treating variables and functions as nodes, we can identify communities (clusters) within a single file.
I integrated this into a tool for Claude Code called VHO. It helps dismantle monolithic React/Vue hooks by grounded mathematical analysis rather than just LLM prediction.
Technical breakdown:
AST Parsing: To build the reference graph. LPA: To find the optimal boundaries for extraction. Agentic Execution: Using Claude Code to perform the actual file I/O and refactoring. Is graph-theory-assisted refactoring the future of AI coding? I'd love to discuss the methodology.
Link: https://github.com/zcf0508/vue-hook-optimizer