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Code as a Graph: Visualizing Software with Neo4j

Exploring how graph databases can revolutionize code analysis and visualization

Powered by Neo4j Graph Database Technology


Presented by Valantine Suh
Associate Full Stack Engineer at adorsys

Introduction

Traditional approaches to code analysis are limited by linear thinking

Graph databases provide a new way to visualize and query code relationships, so today, we'll explore how Neo4j can transform code into a graph

Agenda

  • Why Graph Databases for Code?
  • What are Graph Databases?
  • What is Neo4j?
  • Our Use Case: Indexing Code Repositories
  • Demo: Visualizing Code as a Graph
  • Benefits & Challenges
  • Conclusion
  • Q&A

Visualizing Code as a Graph

Software systems are inherently interconnected - like graphs!

graph TD; A[Traditional Analysis] ---> B[Limited Views] C[Graph-Based Analysis] ---> D[Rich Relationships] C ---> E[Pattern Discovery] C ---> F[Impact Analysis]

Why Graph Databases for Code?

  • Code has natural relationships: imports, dependencies, inheritance
  • Traditional databases struggle with complex interconnections
  • Graph databases excel at traversing relationships
  • Enable powerful queries across the entire codebase

What are Graph Databases?

These are databases that use graph structures with nodes, edges, and properties to represent and store data.


graph LR; A[Node: Class] -->|EXTENDS| B[Node: Parent Class] A -->|IMPLEMENTS| C[Node: Interface] A -->|USES| D[Node: Library] B -->|CONTAINS| E[Node: Method]

Core Components

  • Nodes: Entities (Classes, Methods, Files)
  • Relationships: Connections between entities
  • Properties: Key-value pairs storing data
  • Labels: Categorizing nodes by type

Graph vs Relational

graph TD; subgraph "Relational Database" T1[Table: Classes] T2[Table: Methods] T3[Table: Dependencies] T1 -.-> T2 T2 -.-> T3 end subgraph "Graph Database" N1[Class Node] -->|HAS_METHOD| N2[Method Node] N1 -->|DEPENDS_ON| N3[Library Node] N2 -->|CALLS| N4[Method Node] end

What is Neo4j?

Neo4j

Neo4j is a native graphical database, which implements a true graph model all the way down to the core level, instead of using a graph abstraction on top of another technology.

Key Characteristics

  • Native graph storage and processing
  • ACID (Atomicity, Consistency, Isolation, Durability) compliant
  • Cypher query language
  • Highly scalable and performant

Cypher Query Language

SQL-like syntax for graph queries


MATCH (class:Class)-[:HAS_METHOD]->(method:Method)
WHERE class.name = "UserService"
RETURN method.name, method.complexity

Human-readable pattern matching

Performance Benefits

  • Index-free adjacency: Relationships are stored as pointers
  • Pattern matching: Optimized for complex queries
  • Memory efficiency: Only loads relevant subgraphs

Our Use Case

"Indexing Code Repositories into Neo4j"


Transform code repositories into queryable graph structures

What We Index

graph TD; A[Code] --> B[Files] A --> C[Directories] B --> D[Classes] B --> E[Functions/Methods] B --> F[Variables] D --> G[Dependencies] E --> H[Function Calls] D --> I[Inheritance]

Relationship Types

  • CONTAINS: Directory contains files
  • DEFINES: File defines classes/functions
  • EXTENDS: Class inheritance
  • IMPLEMENTS: Interface implementation
  • CALLS: Function/method invocations
  • IMPORTS: Module dependencies
  • USES: Variable/resource usage

Sample Queries

Find all classes that depend on a specific library: 

MATCH (lib:Library {name: "express"})<-[:IMPORTS]-(file:File)-[:DEFINES]->(class:Class)
RETURN class.name, file.path

Detect circular dependencies:

            MATCH path = (a:Class)-[:DEPENDS_ON*]->(a)
            WHERE length(path) > 1
            RETURN path

Demo Time!

Demo Highlights

  • Import a sample code
  • Visualize the code structure as a graph
  • Run complex queries to analyze dependencies
  • Identify potential refactoring opportunities
  • Show impact analysis for proposed changes

What You'll See

graph LR; A[Code Files] ---> B[Parsed AST] B ---> C[Graph Nodes & Relations] C ---> D[Neo4j Browser] D ---> E[Visual Graph] D ---> F[Query Results]

Benefits & Use Cases

  • Code Reviews: Better understanding of change impact
  • Refactoring: Safe restructuring with dependency awareness
  • Onboarding: Help new developers understand codebase
  • Technical Debt: Identify problematic areas

Challenges & Considerations

  • Parsing Complexity: Different languages, different structures
  • Scale: Large code bases require optimization
  • Maintenance: Keeping graph synchronized with code changes
  • Infrastructure: Neo4j deployment and management

Questions & Answers

Let's discuss your thoughts and explore possibilities!

Common Questions

  • How does this compare to static analysis tools?
  • What's the performance impact on large codebases?
  • Can this integrate with existing CI/CD pipelines?
  • What programming languages are supported?
  • How do we handle dynamically typed languages?

Conclusion

Graph databases open new possibilities for code analysis and visualization

Neo4j provides powerful tools for understanding software architecture leading to ease of implementation and maintenance.

Useful Links

Graph databases open new possibilities for code analysis, below are the links to relevant resources and repositories

Thank you!

Remember: Software is a graph - let's treat it as one!
Ready to start visualizing your code as a graph?

Contact: info.cm@adorsys.com