@misc{16820,
  author = {Xinyi Wang and Paolo Arcaini and Tao Yue and Shaukat Ali},
  title = {QuSBT: Search-Based Testing of Quantum Programs},
  abstract = {Generating a test suite for a quantum program such that it has the maximum number of failing tests is an optimization problem. For such optimization, search-based testing has shown promising results in the context of classical programs. To this end, we present a test generation tool for quantum programs based on a genetic algorithm, called QuSBT (Search-based Testing of Quantum Programs). QuSBT automates the testing of quantum programs, with the aim of finding a test suite having the maximum number of failing test cases. QuSBT utilizes IBM{\textquoteright}s Qiskit as the simulation framework for quantum programs. We present the tool architecture in addition to the implemented methodology (i.e., the encoding of the search individual, the definition of the fitness function expressing the search problem, and the test assessment w.r.t. two types of failures). Finally, we report results of the experiments in which we tested a set of faulty quantum programs with QuSBT to assess its effectiveness. 
Repository (code and experimental results): https://github.com/Simula-COMPLEX/qusbt-tool 
Video: https://youtu.be/3apRCtluAn4},
  year = {2022},
  journal = {2022 IEEE/ACM 44th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)},
  publisher = {IEEE},
  url = {https://ieeexplore.ieee.org/document/9793826},
  doi = {10. 1145/3510454.3516839},
}