Tao Yue’s present research area is software engineering, with specific interests in Model-based Engineering, Software Testing, Uncertainty-wise Software Engineering, Search-based Software Engineering, and Quantum Software Engineering.
QuCAT: A Combinatorial Testing Tool for Quantum Software
With the increased developments in quantum computing, the availability of systematic and automatic testing approaches for quantum programs is becoming increasingly essential. To this end, we present the quantum software testing tool QuCAT for combinatorial testing of quantum programs. QuCAT provides two functionalities of use. With the first functionality, the tool generates a test suite of a given strength (e.g., pair-wise). With the second functionality, it generates test suites with increasing strength until a failure is triggered or a maximum strength is reached. QuCAT uses two test oracles to check the correctness of test outputs. We assess the cost and effectiveness of QuCAT with 3 faulty versions of 5 quantum programs. Results show that combinatorial test suites with a low strength can find faults with limited cost, while a higher strength performs better to trigger some difficult faults with relatively higher cost. Repository: https://github.com/qiqihannah/QuCAT-Tool Video: https://youtu.be/UsqgOudKLio
Uncertain, unpredictable, real-time, and lifelong evolution causes operational failures in intelligent software systems, leading to significant damages, safety and security hazards, and tragedies. To fully unleash such systems’ potential and facilitate their wider adoption, ensuring the trustworthiness of their decision-making under uncertainty is the prime challenge. To overcome this challenge, an intelligent software system and its operating environment should be continuously monitored, tested, and refined during its lifetime operation. Existing technologies, such as digital twins, can enable continuous synchronisation with such systems to reflect their most up-to-date states. Such representations are often in the form of prior-knowledge-based and machine-learning models, together called ‘model universe’. In this paper, we present our vision of combining techniques from software engineering, evolutionary computation, and machine learning to support the model universe evolution.
