QUANTUM COMPUTING FOR TEST OPTIMIZATION IN SAFETY-CRITICAL SYSTEMS

Abstract

The increasing complexity of software in safety-critical domains like aerospace and automotive demands more efficient testing methods. Traditional test optimization struggles to balance fault detection with resource limits. This research proposes a quantum computing-based framework for test case prioritization and selection, leveraging algorithms such as Grover’s Search and QAOA. By integrating classical fault-based testing with quantum techniques, the approach aims to reduce test suite size while maintaining or improving fault detection. Experiments using benchmark datasets (e.g., Siemens suite) on simulators and IBM Q hardware show that quantum methods can outperform traditional approaches in fault coverage, suite reduction, and efficiency, especially for large test spaces. Statistical analyses validate these improvements. Despite current quantum hardware limitations, the study highlights the promising role of quantum computing in optimizing software testing for safety-critical systems and outlines future directions for advancing this field.