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Tetraband - Test Transformation Bandit

Tetraband is an implementation of Adaptive Metamorphic Testing (AMT). AMT extends traditional metamorphic testing to select one of the multiple metamorphic relations available for a program. By using contextual bandits, AMT learns which metamorphic relations are likely to transform a source test case, such that it has higher chance to discover faults.

More details are in the preprint of the paper Adaptive Metamorphic Testing with Contextual Bandits.

Running Tetraband

Example commandline call:

python run.py --environment classification --scenario hierarchical --dataset cifar10 --agent bandit --iterations 5000

List available options: python run.py --help

Environments

We include OpenAI Gym environments, i.e. image classification and object detection (see code in envs). Each of the environments has several options, which specify which dataset is used and which actions are available.

These environments can be used by importing the envs module and instantiating the wanted configuration:

import gym
import envs
env = gym.make('ImageClassificationEnv-basic-cifar10-v0')
print("Number of actions: ", env.action_space.n)
print("Observation space: ", env.observation_space)
print("Actions: ", env.action_names())

Datasets

Available datasets (will be automatically downloaded):

  • CIFAR-10 (image classification)
  • Imagenet (image classification)
  • MS COCO (object detection)

Configurations

  • basic: 9 MRs (Blur, Flip L/R, Flip U/D, Grayscale, Invert, Rotate(-30), Rotate(30), Shear(-20), Shear(20))
  • rotation: Rotate(-90), Rotate(-85), Rotate(-80), ..., Rotate(80), Rotate(85), Rotate(90) (except Rotate(0))
  • shear: Shear(-45), Shear(-40), Shear(-35), ..., Shear(35), Shear(40), Shear(45) (except Shear(0))
  • hierarchical: A combination of basic, rotation, and shear.

Requirements

Tetraband was developed and tested under Python 2.7 and might not work with Python 3.

Most required packages are installed via pip -r requirements.txt

Installing vowpal wabbit requires Boost. If you are using anaconda, you can run conda install boost and export BOOST_LIBRARYDIR=$CONDA_PREFIX/libs/ before installation.

For the object detection environments, you additionally need pycocotools, which can be installed either via

  1. conda install -c conda-forge pycocotools
  2. Cloning the repository and running make.

Object Detection API

The object detection environment is based on the Tensorflow Object Detection API from tensorflow/models.

This is already setup, the links below are for reference. Installation guide

A good introduction is given in their tutorial.

In case of problems, ensure the object detection API is included in $PYTHONPATH. But this is mostly handled by having the object_detection package copied into this project.

License

MIT License