Guess What Quantum Computing Can Do for Test Case Optimization

• URL: http://arxiv.org/abs/2312.15547v1
• Date: Sun, 24 Dec 2023 21:25:31 GMT
• Title: Guess What Quantum Computing Can Do for Test Case Optimization
• Authors: Xinyi Wang, Shaukat Ali, Tao Yue, Paolo Arcaini
• Abstract summary: In the near term, quantum approximate optimization algorithms (QAOAs) hold great potential to solve optimization problems. We present the first effort to formulate a software test case optimization problem as a QAOA problem and solve it on quantum computer simulators. To solve bigger test optimization problems that require many qubits, which are unavailable these days, we integrate a problem decomposition strategy with the QAOA.
• Score: 43.89456212504871
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the near term, quantum approximate optimization algorithms (QAOAs) hold great potential to solve combinatorial optimization problems. These are hybrid algorithms, i.e., a combination of quantum and classical algorithms. Several proof-of-concept applications of QAOAs for solving combinatorial problems, such as portfolio optimization, energy optimization in power systems, and job scheduling, have been demonstrated. However, whether QAOAs can efficiently solve optimization problems from classical software engineering, such as test optimization, remains unstudied. To this end, we present the first effort to formulate a software test case optimization problem as a QAOA problem and solve it on quantum computer simulators. To solve bigger test optimization problems that require many qubits, which are unavailable these days, we integrate a problem decomposition strategy with the QAOA. We performed an empirical evaluation with five test case optimization problems and four industrial datasets from ABB, Google, and Orona to compare various configurations of our approach, assess its decomposition strategy of handling large datasets, and compare its performance with classical algorithms (i.e., Genetic Algorithm (GA) and Random Search). Based on the evaluation results, we recommend the best configuration of our approach for test case optimization problems. Also, we demonstrate that our strategy can reach the same effectiveness as GA and outperform GA in two out of five test case optimization problems we conducted.

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