Artifact: NaturalFuzz: Natural Input Generation for Big Data Analytics

1. Pre-requisites

This manual assumes docker is installed and set up properly on your device.
These instructions have been tested with the following configurations:

• Ubuntu: 20.04.6 LTS
  Docker: 24.0.2, build cb74dfc

2. Creating the Docker Image

Clone this repository:

git clone https://github.com/SEED-VT/NaturalFuzz.git

Navigate into the repository folder:

cd NaturalFuzz

Build the docker image using:

NOTE: Depending on how docker is configured, you may need to sudo this

docker build . -t naturalfuzz --no-cache

TROUBLESHOOTING: If the above command fails try restarting the docker service using: sudo systemctl restart docker

3. Running the Container

Obtain a shell instance inside the docker container:

docker run -v $(pwd)/graphs:/NaturalFuzz/graphs -it naturalfuzz bash

4. Running the Experiments

NOTE: All following commands must be run on the shell instance inside the docker container

Navigate into the repository folder:

cd NaturalFuzz

The following is the template command for running any of the benchmark programs:

./run-fuzzer.sh <PROGRAM_NAME> <DURATION> <DATASETPATH_1> ... <DATASETPATH_N>

For PROGRAM_NAME you may pass in the name of any scala file under src/main/scala/examples/tpcds/ after omitting the extension

We will show you how to run the tool for Q1. To fuzz Q1 for 5 minutes, run:

./run-fuzzer.sh Q1 300 data/{store_returns,date_dim,store,customer}

5. Computing Perplexity Scores