Repository showcasing an example use of the Plume library.

All known issues can be found here.

This section outline the requirements and setup involved before using this application.

• git
• docker
• docker-compose
• java (11 )
• sbt

Replace X.X.X with the desired version on .

libraryDependencies   = Seq(
  com.github.plume-oss % plume % X.X.X
)

resolvers  = "jitpack" at "https://jitpack.io"

Example code to analyze can be found under example. One needs to first compile the .java source code files e.g. javac -g *.java.

Additionally, one can use OverflowDbDriver to perform basic taint-analysis with reachability queries.

An example follows the name convention of com.example.*App.scala where their main method can be run using:

sbt clean compile
sbt "runMain com.example.*App"

e.g.

sbt "runMain com.example.OverflowDbApp"

The basic operation that the com.example.*App.scala classes do is:

1. Create a driver that connects to the selected graph database backend.
2. Loads a directory with class files to project to the database.
3. TinkerGraph and OverflowDb can serialize and deserialize the CPG to files whereas the other databases store the graphs onto disk in their systems.

One can then visualize this graph using a visualization tool such as Cytoscape.

"TinkerGraph is a lightweight, POJO based, in-memory property graph that serves as the reference implementation for the property graph model."

The TinkerGraph example can be run using com.example.TinkerGraphApp::main.

"ShiftLeft's OverflowDB is an in-memory graph database, which implements a swapping mechanism to deal with large graphs."

The OverflowDb example can be run using com.example.OverflowDbApp::main. To serialize/de-serialize the database, the location needs to be specified in the constructor.

If the file does not exist, then it will be created. If it does exist then that CPG will be imported.

TigerGraph is the fastest and most scalable graph database analytics platform—and the only native parallel graph database.

To run with TigerGraph as the graph database backend, start by running the database with:

docker-compose -f src/main/resources/tigergraph.yml up

Running the TigerGraph example:

1. Start TigerGraph using docker-compose. This will install the schema and queries. Make sure the queries have finished installing before moving to the next step.
2. Project a program's CPG into TigerGraph with com.example.TigerGraphApp::main
3. Open your web-browser and navigate to http://localhost:14240/ to view GraphStudio where you can play around with the CPG.

Amazon Neptune is a fast, reliable, fully managed graph database service that makes it easy to build and run applications that work with highly connected datasets.

To communicate with a Neptune cluster, one needs to run this examples repository from within an EC2 instance on the same VPC as the Neptune cluster. More on how this works can be found in the documentation.

Running the Neptune example:

1. Make sure the Neptune cluster is up and fill in the missing parameters under com.example.NeptuneApp.
2. Project a program's CPG into Neptune with com.example.NeptuneApp::main

Neo4j is the graph database platform powering mission-critical enterprise applications like artificial intelligence, fraud detection and recommendations.

To run with Neo4j as the graph database backend, start by running the database with:

docker-compose -f src/main/resources/neo4j.yml up

This starts up the Bitnami Neo4j image. Running the Neo4j example:

1. Project a program's CPG into Neo4j with com.example.Neo4jApp::main

2. View the CPG by navigating to http://localhost:7474 and (if required) log in with the following credentials

   Username: neo4j

   Password: neo4j