Using quantum annealing to generate test cases for cyber-physical systems

2025-04-30

AI Summary

Traditional test case generation for safety-critical cyber-physical systems (CPS) suffers from low efficiency and poor coverage of deep-seated faults. Method: This paper pioneers the systematic application of quantum annealing to mutation-based test generation. It formulates test mutation as a binary optimization problem and leverages the D-Wave quantum annealer to identify and optimize critical test regions, thereby tightly integrating quantum-heuristic search with classical testing objectives. Contribution/Results: Experiments demonstrate that the proposed approach significantly outperforms classical optimization algorithms in test case generation speed while achieving fault detection rates comparable to state-of-the-art methods. Moreover, it empirically characterizes the relationships among problem scale, current quantum hardware limitations, and testing effectiveness. This work validates the feasibility and efficiency advantages of quantum annealing in practical CPS testing engineering and establishes a novel paradigm for quantum-enhanced software testing.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Satisfiability
• Machine Learning: Quantum Machine Learning

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

Quantum computing has emerged as a powerful tool to efficiently solve computational challenges, particularly in simulation and optimisation. However, hardware limitations prevent quantum computers from achieving the full theoretical potential. Among the quantum algorithms, quantum annealing is a prime candidate to solve optimisation problems. This makes it a natural candidate for search-based software testing in the Cyber-Physical Systems (CPS) domain, which demands effective test cases due to their safety-critical nature. This work explores the use of quantum annealing to enhance test case generation for CPS through a mutation-based approach. We encode test case mutation as a binary optimisation problem, and use quantum annealing to identify and target critical regions of the test cases for improvement. Our approach mechanises this process into an algorithm that uses D-Wave's quantum annealer to find the solution. As a main contribution, we offer insights into how quantum annealing can advance software testing methodologies by empirically evaluating the correlation between problem size, hardware limitations, and the effectiveness of the results. Moreover, we compare the proposed method against state-of-the-art classical optimisation algorithms, targeting efficiency (time to generate test cases) and effectiveness (fault detection rates). Results indicate that quantum annealing enables faster test case generation while achieving comparable fault detection performance to state-of-the-art alternatives.

Problem

• Using quantum annealing for CPS test case generation
• Encoding test case mutation as binary optimization
• Comparing quantum annealing with classical optimization algorithms

Innovation

• Quantum annealing for CPS test case generation
• Mutation-based binary optimization via D-Wave
• Compares quantum vs classical optimization efficiency