|Authors||X. Wang, T. Yu, P. Arcaini, T. Yue and S. Ali|
|Title||Mutation-Based Test Generation for Quantum Programs with Multi-Objective Search|
|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|
Mutation testing is often used for designing new tests, and involves changing a program in minor ways, which results in mutated versions of the program, i.e., mutants. An effective test suite should find faults (or kill mutants) with a minimum number of test cases, to save resources required for executing test cases. In this paper, in the context of mutation testing for quantum programs, we present a multi-objective and search-based approach (MutTG) to generate the minimum number of test cases killing as many mutants as possible. MutTG tries to estimate the likelihood that a mutant is equivalent, and uses this as a discount factor in the fitness definition to avoid keeping on trying to kill mutants that cannot be killed. We employed NSGA-II as the multi-objective search algorithm. Then, we compared MutTG with another version of the approach that does not use the discount factor in its fitness definition, and with random search (RS), over a set of open-source quantum programs and their mutants of varying complexity. Results show that the discount factor does indeed help in guiding the test generation, as the approach with the discount factor performs better than the one without it.