基于搜索的未被检测到的量子电路变异体生成
Search-Based Generation of Undetected Quantum Circuit Mutants
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中文总结 AI 辅助
针对现有量子变异分析工具生成的变异体易检测的问题，提出QUMUG方法，通过搜索算法生成更具挑战性的未被检测的量子电路变异体，提升测试套件评估效果。
中文摘要 AI 辅助
量子变异分析因真实故障量子程序的可用性有限，正成为评估测试套件的重要技术。然而，现有的量子变异分析工具使用固定的基于门的变异，生成的变异体易于检测，降低了其评估测试套件质量的有效性。我们提出QUMUG，一种利用可参数化量子门生成具有挑战性变异体的基于搜索的方法。QUMUG采用搜索算法优化变异参数，找到通过给定测试套件的非等价变异体。在对30个量子程序的评估中，QUMUG生成的变异体比现有工具生成的变异体挑战性高3倍。在评估的四种搜索算法中，遗传算法最有效，平均每个程序生成494个未被检测到的变异体，成功率为99.67%，非等价率为94.3%。生成的变异体被证明有效，与现有工具生成的变异体相比，测试套件需要增加5倍的测试用例。我们还分析了量子电路中高阶变异体的行为，结果表明，虽然一阶变异体对增强测试套件更有效，但高阶变异体凸显了对新的独特测试用例的需求。
英文摘要
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.
