|Authors||X. Wang, P. Arcaini, T. Yue and S. Ali|
|Title||Generating Failing Test Suites for Quantum Programs with Search (hot off the press track at GECCO 2022)|
|Project(s)||Department of Engineering Complex Software Systems, Quantum Software Engineering Project, Enabling Future Dependable Ubiquitous Services and Data with Novel Testing Methods for Quantum Programs|
|Publication Type||Proceedings, refereed|
|Year of Publication||2022|
|Conference Name||GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference|
The inherent complexity of quantum programs, due to features such as superposition and entanglement, makes their testing particularly challenging. To tackle these challenges, we present a search-based approach, called Quantum Search-Based Testing (QuSBT), for automatically generating test suites of a given size that possibly expose failures of the quantum program under test. QuSBT encodes a test suite as a search individual, and tries to maximize the objective function that counts the number of failing tests in the test suite. Due to non-deterministic nature of quantum programs, the approach repeats the execution of each test multiple times, and uses suitable statistical tests to assess if a test passes or fails. QuSBT employs a genetic algorithm to perform the search. Experiments on 30 faulty quantum programs show that QuSBT is statistically better than random search, and is able to efficiently generate maximal failing test suites.
This is an extended abstract of the paper : X. Wang, P. Arcaini, T. Yue, and S. Ali "Generating Failing Test Suites for Quantum Programs With Search", 13th International Symposium on Search-Based Software Engineering (SSBSE 2021).