Codes for paper: Correlations Between Deep Neural Network Model Coverage Criteria and Model Quality

Code has been tested on:

• Python 3.6, 3.7
• OS: Ubuntu 18.04

We have prepared a docker which contains all deps:

# Get the environment (OS) to run the code
$ docker pull hao359510974/covtesting2:latest
$ docker run -it --mount type=bind,src=SRC_PATH,dst=DEST_PATH hao359510974/covtesting2:latest # Where SRC_PATH and DEST_PATH must be absolute paths; SRC_PATH is the path on your host machine, and DEST_PATH is the file path for where you want it to be stored in the Docker, such as '/data'.
    
# Experiments (take Comparison of Attack Images (Table 2) as an example, for other experiments just use other folders.)
$ cd /data/Comparison\ of\ Attack\ Images/

# Then run commands according to 'README' file to get corresponding results. 

We also provide requirements.txt file for users who want to establish the environment on their own machine:

$ pip install -r requirements.txt

- code/                                                  # Source code for Covtesting
	- Comparison of Attack Images/                   # Codes folder for comparison of attack images (Table 2)
	- Correlation                                    # Codes folder for calculation of correlation (Figure 4 and figure 8)
	- Coverage VS. # Adv Examples                    # Codes folder for coverage VS. # adv examples (Figure 6 and figure 7)
	- Coverage and Robustness VS. Training Datasets  # Codes folder for coverage and robustness VS. training datasets (Figure 2 and figure 3)
	- Model Accuracy under Different Scenarios       # Codes folder for model accuracy under different scenarios (Table 3)
	- data                                           # Reserved data folder for 'Correlation', 'Coverage VS. # Adv Examples' and 'Coverage and Robustness VS. Training Datasets'
- CONTACT                                                # Contact information of authors
- LICENSE                                                # Our code is released under the MIT license
- README                                                 # The README.md file
- esecfse2020-paper53.pdfA                               # A copy of our accepted paper
- index                                                  # The DOI of codes and data
- requirements.txt                                       # Required dependencies

• Download data and codes through the DOI link:
• Google Drive mirror: https://drive.google.com/drive/folders/16w93LPkaF0AP9QxIty9Y6ipU-N4cbPUd?usp=sharing

You will get two zip files: 'all-data.zip' (12.2 G, the data file) and 'DNNTesting/CovTesting-v1.1.zip' (5.2 M, the codes file). Please unzip the codes and data files to get the codes and data for experiments. After unzipping 'all-data.zip', you will get three zip files named 'data.zip' (4.1 G), 'Table 2 data.zip' (3.5 G) and 'Table 3 data.zip' (4.6 G).

• Unzip 'data.zip' and get three folders: 'cifar_data', 'mnist_data' and 'svhn_data'. Please put these three folders under the 'data' folder in the main folder.
• Unzip 'Table 2 data.zip' and get three folders: 'cifar_data', 'mnist_data' and 'svhn_data'. Please put these three folders under the 'data' folder under the 'Comparison of Attack Images' folder.
• Unzip 'Table 3 data.zip' and get two folders: 'data' and 'new_model'. Please merge these two folders into the 'data' folder and the 'new_model' folder under the 'Model Accuracy under Different Scenarios' folder, respectively.

Details of how to run each experiment are written as README files in each folder. Users can customize the scripts to run their own data based on provided instructions.

Results of using provided data can reproduce experimental results in the paper.

Please fill an issue here if you have questions regarding the code.

• Shenao Yan (sy558 AT rutgers DOT edu)
• Shiqing Ma (shiqing.ma AT rutgers DOT edu)