Mutation-Based Test Generation for Quantum Programs with Multi-Objective Search

Installation

• Install Anaconda3. You can download Anaconda for your OS from https://www.anaconda.com/
• Create an environment (e.g., with name "myenv")
  • conda create -n myenv python=3.7 scipy numpy rpy2
• Activate the environment, and install qiskit, jmetalpy and openpyxl
  • conda activate myenv
  • pip install qiskit
  • pip install jmetalpy
  • pip install openpyxl
• In your Anaconda installation, change the file envs/myenv/lib/python3.7/site-packages/jmetal/operator/init.py
  • "DifferentialEvolutionCrossover" must be changed in "DifferentialEvolutionCrossover, IntegerSBXCrossover"

Running experiment

First, you need to activate the conda environment:
conda activate myenv
Then, you can start the program (from the 'code' root) as follows:

MutTG

python main_int.py program_name difficult_level input_bit output_bit mutant_num k file_name algorithm
where

• program_name is the name of the original program. Available program in the repository are: AS, BV, CE, IQ, and QR
• difficult_level can be easy, medium, difficult1, or difficult3 (that correspond to E, M, D1, and D3 in the paper)
• input_bit is the number of input qubits of the program.
• output_bit is the number of output qubits of the program.
• mutant_num is the number of mutants of the program.
• k is the maximum length k of an individual (that also identifies the maximum possible size of the test suite)
• file_name is the name of the generated excel file. Each excel workbook contains 5 sheets: gen_test, gen_test_count, FUN_test, VAR_test and objective log.
• algorithm is either 'Search' or 'RS' to run NSGA-II or random search.

MutTG without discount

python main_int_adj.py program_name difficult_level input_bit output_bit mutant_num k file_name algorithm