TY - GEN
T1 - QuSBT
T2 - 44th ACM/IEEE International Conference on Software Engineering: Companion proceedings, ICSE-Companion 2022
AU - Wang, Xinyi
AU - Arcaini, Paolo
AU - Yue, Tao
AU - Ali, Shaukat
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022/10/19
Y1 - 2022/10/19
N2 - 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'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
AB - 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'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
KW - Genetic Algorithms
KW - Quantum Programs
KW - Search-Based Testing
UR - https://www.scopus.com/pages/publications/85132388747
U2 - 10.1109/ICSE-Companion55297.2022.9793826
DO - 10.1109/ICSE-Companion55297.2022.9793826
M3 - 会议稿件
AN - SCOPUS:85132388747
T3 - Proceedings - International Conference on Software Engineering
SP - 173
EP - 177
BT - Proceedings - 2022 ACM/IEEE 44th International Conference on Software Engineering
PB - IEEE Computer Society
Y2 - 22 May 2022 through 27 May 2022
ER -