# deepTest

**Repository Path**: alex_wonga/deepTest

## Basic Information

- **Project Name**: deepTest
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-03-26
- **Last Updated**: 2022-03-26

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# DeepTest: Automated testing of deep-neural-network-driven autonomous cars

DeepTest is a systematic testing tool for automatically detecting erroneous behaviors of DNN-driven vehicles that can potentially lead to fatal crashes.

## Install Required Packages

OS: Ubuntu 16.04  
Read through and run [./install.sh](./install.sh)

## Code Directories

[models/](models/)  

* Reproducing Udacity self-driving car Challenge2<sup>[1]</sup> results for Rambo, Chauffeur and Epoch models.  
* [Models and weights files](https://github.com/ARiSE-Lab/deepTest#models-and-saved-weights) are required to run these scripts.
* For Rambo model, Keras backend should be changed to theano.  

[testgen/](testgen/)
* Generate synthetic images, calculate cumulative coverage and record predicted outputs.
* [ncoverage.py](testgen/ncoverage.py): define NCoverage class for computing neuron coverage. 

[guided/](guided/)  
* Combine different transformations and leverage neuron coverage to guide the search.
* Re-runing the script will continue the search instead of starting from the beginning except deleting all pkl files.

[metamorphictesting/](metamorphictesting/) 

## Models and [Saved Weights](https://github.com/udacity/self-driving-car/tree/master/steering-models/evaluation)    
* Rambo <sup>[2]</sup>  
* Chauffeur <sup>[3]</sup>
* Epoch <sup>[4]</sup>

 
## Datasets

* [HMB_3.bag](https://github.com/udacity/self-driving-car/blob/master/datasets/CH2/HMB_3.bag.tar.gz.torrent): Test dataset  
* [CH2_001](https://github.com/udacity/self-driving-car/tree/master/datasets/CH2): Final Test Data for challenge2
  * [CH2_001 labels](https://github.com/udacity/self-driving-car/blob/master/challenges/challenge-2/CH2_final_evaluation.csv)

### Generate hmb3 dataset from HMB_3.bag

```
mkdir hmb3
python generate_hmb3.py --bagfile HMB_3.bag
```
The following commands are to install python-rosbag api.
```
sudo apt-get install python-rosbag
sudo apt-get install python-genmsg
sudo apt-get install python-genpy
sudo apt-get install python-rosgraph-msgs
```
## Reproducing experiments

### Dataset directory structure:  
./Dataset/   
├── hmb3/  
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;  └── hmb3_steering.csv  
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;  └── images  
└── Ch2_001/  
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;  └── center/  
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; └── images  
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;  └── CH2_final_evaluation.csv  

### Evaluating models' accuracy on existing test data
```
cd models/
python epoch_reproduce.py --dataset DATA_PATH/Dataset/
python chauffeur_reproduce.py --dataset DATA_PATH/Dataset/
python rambo_reproduce.py --dataset DATA_PATH/Dataset/
```
### Generate synthetic images and compute cumulative neuron coverage
```
cd testgen/
./epoch_testgen_driver.sh 'DATA_PATH/Dataset/'
./chauffeur_testgen_driver.sh 'DATA_PATH/Dataset/'
python rambo_testgen_coverage.py --dataset DATA_PATH/Dataset/
```
### Combine transformations and synthesize images by guided search
```
cd guided/
mkdir new/
rm -rf *.pkl
python epoch_guided.py --dataset DATA_PATH/Dataset/
python chauffeur_guided.py --dataset DATA_PATH/Dataset/
python rambo_guided.py --dataset DATA_PATH/Dataset/
```
<!---### Identify erroneous behaviors by metamorphic testing--->

## Detected erroneous behaviors
https://deeplearningtest.github.io/deepTest/

## Citation
If you find DeepTest useful for your research, please cite the following [paper](https://arxiv.org/pdf/1708.08559.pdf):

```
@article{tian2017deeptest,
  title={DeepTest: Automated testing of deep-neural-network-driven autonomous cars},
  author={Tian, Yuchi and Pei, Kexin and Jana, Suman and Ray, Baishakhi},
  journal={arXiv preprint arXiv:1708.08559},
  year={2017}
}

```
## References

1.  **Udacity self driving car challenge 2**. <br /> 
https://github.com/udacity/self-driving-car/tree/master/challenges/challenge-2. (2016).
2.  **Rambo model**. <br />
https://github.com/udacity/self-driving-car/tree/master/steering-models/community-models/rambo. (2016).
3.  **Chauffeur model**. <br />
https://github.com/udacity/self-driving-car/tree/master/steering-models/community-models/chauffeur. (2016).
4.  **Epoch model**. <br />
https://github.com/udacity/self-driving-car/tree/master/steering-models/community-models/cg23. (2016).