Title:Search-Based Generation of Undetected Quantum Circuit Mutants

Abstract:Quantum mutation analysis is emerging as an essential technique for evaluating test suites due to the limited availability of real faulty quantum programs. However, existing quantum mutation analysis tools use fixed gate-based mutations, resulting in easy-to-detect mutants, which reduces their effectiveness in assessing the quality of test suites. We propose QUMUG, a search-based approach for generating challenging mutants by utilising parameterisable quantum gates. QUMUG employs search algorithms to optimise mutation parameters and find non-equivalent mutants passing a given test suite. In our evaluation over 30 quantum programs, QUMUG produced mutants that are three times more challenging than the mutants generated by existing tools. Among the four evaluated search algorithms, the genetic algorithm was the most effective, generating an average of 494 undetected mutants per program with a 99.67% success rate and 94.3% non-equivalent ratio. The generated mutants demonstrated their effectiveness by requiring the addition of five times more test cases to the test suite than the mutants generated by existing tools. We also analysed the behaviour of higher order mutants in quantum circuits, and showed that while first order mutations are more effective for enhancing the test suite, higher order mutants highlight the need for new unique test cases.