AuthorsX. Wang, P. Arcaini, T. Yue and S. Ali
TitleQuSBT: Search-Based Testing of Quantum Programs
AfilliationSoftware Engineering
Project(s)Department of Engineering Complex Software Systems, Enabling Future Dependable Ubiquitous Services and Data with Novel Testing Methods for Quantum Programs, Quantum Software Engineering Project
StatusPublished
Publication TypeProceedings, refereed
Year of Publication2022
Conference Name2022 IEEE/ACM 44th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)
PublisherIEEE
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’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

URLhttps://ieeexplore.ieee.org/document/9793826
DOI10. 1145/3510454.3516839
Citation Key42901

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