codepropertygraph 0.1.2

A Python implementation of a Code Property Graph.

Code Property Graph

This library is an implementation of a Code Property Graph as seen in the paper published by Fabian Yamaguchi on Modeling and Discovering Vulnerabilities with Code Property Graphs

A code property graph is a highly efficient data structure designed to mine large codebases for similar programming patterns. The data structure can be loaded into a graph database where properties of code can be queried. Code property graphs are intended to be code-agnostic and highly scalable making it one of the best choices for code representation.

Running as a Library

Installation

Requires:

• Python 3
• pip3

pip install codepropertygraph

Using the code as a library

import os
from dotenv import load_dotenv
from codepropertygraph import get_neo4j_connection

load_dotenv()
USERNAME = os.environ["NEO4J_USERNAME"]
PASSWORD = os.environ["NEO4J_PASSWORD"]
URI = "neo4j+s://cb8ae961.databases.neo4j.io"

# Attempt to get a connection
driver = get_neo4j_connection(URI, (USERNAME, PASSWORD))

# If the connection is successful, you can use the driver
if driver:
    with driver.session(database="neo4j") as session:
        result = session.run("MATCH (n) RETURN count(n) AS node_count")
        node_count = result.single()["node_count"]
        print(f"Number of nodes in the database: {node_count}")
    
    driver.close()

> Number of nodes in the database: 0