Test case optimization (TCO) reduces software testing cost while preserving its effectiveness, but solving TCO problems for large-scale and complex systems requires substantial computational resources. Quantum approximate optimization algorithms (QAOAs) are promising combinatorial optimization algorithms that rely on quantum computational resources, with the potential efficiency advantages over classical approaches. Several proof-of-concept applications of QAOAs for solving combinatorial problems, such as portfolio optimization, energy systems, and job scheduling, have been proposed. Given the lack of investigation into QAOA's application to TCO problems, and motivated by the computational challenges of TCO problems and the potential of QAOAs, we present IGDec-QAOA to formulate a TCO problem as a QAOA problem and solve it on both ideal and noisy quantum computer simulators, as well as on a real quantum computer. To solve bigger TCO problems that require many qubits, which are unavailable currently, we integrate a problem decomposition strategy with the QAOA. We performed an empirical evaluation with five TCO problems and four publicly available industrial datasets from ABB, Google, and Orona to compare various configurations of IGDec-QAOA, assess its decomposition strategy of handling large datasets, and compare its performance with classical algorithms (i.e., GA and Random Search). Based on the evaluation results achieved on an ideal simulator, we recommend the best configuration of our approach for TCO problems. We also demonstrate that it can reach the same effectiveness as GA and outperform GA in two out of five test case optimization problems. In addition, we observe that, on a noisy simulator, IGDec-QAOA achieved similar performance to that from an ideal simulator. Finally, we demonstrate the feasibility of IGDec-QAOA on a real quantum computer in the presence of noise.

翻译：测试用例优化（TCO）旨在降低软件测试成本，同时保持其有效性，但针对大规模复杂系统求解TCO问题需要大量计算资源。量子近似优化算法（QAOA）是一类有前景的组合优化算法，其依赖于量子计算资源，相比经典方法可能具有效率优势。目前已提出若干QAOA在组合问题（如投资组合优化、能源系统与作业调度）中的概念验证应用。鉴于QAOA在TCO问题中的应用尚缺乏研究，且受TCO问题的计算挑战与QAOA潜力的驱动，本文提出IGDec-QAOA，将TCO问题建模为QAOA问题，并在理想与含噪声量子计算机模拟器以及真实量子计算机上求解。为求解需要大量量子比特（当前尚不可用）的更大规模TCO问题，我们将问题分解策略与QAOA相结合。我们使用五个TCO问题以及来自ABB、Google和Orona的四个公开工业数据集进行了实证评估，以比较IGDec-QAOA的不同配置、评估其处理大型数据集的分解策略，并将其性能与经典算法（即遗传算法和随机搜索）进行对比。基于在理想模拟器上获得的评估结果，我们为TCO问题推荐了所提方法的最佳配置。我们还证明，在五个测试用例优化问题中的两个问题上，该方法能达到与遗传算法相同的有效性且表现更优。此外，我们观察到在含噪声模拟器上，IGDec-QAOA取得了与理想模拟器相近的性能。最后，我们验证了IGDec-QAOA在存在噪声的真实量子计算机上的可行性。