As quantum software engineering advances, testing techniques are required to assess the quality of quantum programs (QPs). In the test process, the test oracle is vital for determining whether the test result indicates a success or a failure. Most related works directly measure the output states and acquire the corresponding test results by comparing the output distribution with the expected one. While attention has been paid to the capability of fault detection, the guarantee for the correctness of the produced test results remains limited. Unlike classical programs (CPs), the output quantum states of QPs should be transformed into probabilistic classical outcomes through quantum measurement. This additional operation of measurement could cause a test oracle to yield the wrong test results. Especially for high-dimensional output spaces, numerous measurement outcomes are required to capture the distribution characteristics, threatening the effectiveness and cost-efficiency of test oracles. Hence, this paper proposes a novel specified test oracle DOSS employing a dynamic scheme to integrate a quantum algorithm (i.e., swap test) with the direct measurement mode. This innovative approach enables the validation of individual outputs rather than their distribution during the testing phase. Considering acceptable cost, DOSS decomposes the fully or partially separable output states to lower the dimensionality and simplify the quantum circuit for testing. Empirical studies demonstrate that DOSS generally gives more correct test results than baselines, and maintains reasonable cost on an ideal simulator. Besides, DOSS’s effectiveness with quantum noise involved is validated via three noisy simulators.

Quantum computing has recently experienced rapid advancements and promised transformative applications across many fields. To promote the real-world applications of quantum computing, application-oriented quantum programs (AQPs) are designed to explore quantum hardware in performing substantial computational tasks and promote practical use cases for quantum computing. The complexity and scalability of AQPs, along with their dependence on advanced quantum algorithms, make them particularly challenging to understand and maintain. Clear comments offer an effective means of elucidating core logic and filling the knowledge gap for developers unfamiliar with quantum mechanics. Research on code comment for QPs remains scarce, highlighting the need for further investigation into effective comment methods for these complex programs. Given the potential advantages of large language models (LLMs), including their contextual understanding and language generation capabilities, LLMs can significantly reduce the cost of manual comment. Thus, this paper proposes a framework called QuAInth, which utilizes LLMs to annotate AQPs. This framework begins by preprocessing AQPs and segmenting them based on functional signatures. It then employs prompts (i.e., textual instructions carefully structured and given to LLMs) of varying granularity to guide comment generation. Aside from two existing text-based metrics, QuAInth newly adopts a quantum-specific metric that considers 8 indicators to evaluate the domainrelated correctness and clarity of the generated comments. Finally, with the understanding that an individual LLM may produce wrong outputs, QuAInth proposes a vote-enhanced fusion scheme inspired by N-version programming, in which distinct comments output from multiple LLMs are fused into a more reliable and comprehensive comment. Empirical studies are conducted with 4 AQPs written by Qiskit and 3 prevailing open-source LLMs (i.e., Qwen, DeepSeek-Coder, and Llama). The empirical results demonstrate the effectiveness of QuAInth, showing that the fused comments outperform those generated by individual models in the vast majority of cases.

Due to the growing demand for high-quality quantum programs (QPs), unit testing is employed to check the behavior of QPs. As for quantum inputs of testing, most studies limit test inputs to pure states, whereas mixed states representing probabilistic mixtures of pure states are almost excluded from the test process. Besides, when achieving the input domain coverage, lots of pure-state test cases (PSTCs) with pure states as inputs should be employed, leading to high time costs for testing. To handle that, this article explores using mixed states as test inputs for better utilization of quantum information. From the perspective of input domain coverage, applying mixed-state test cases (MSTCs) replacing PSTCs can simplify the test suite and accordingly promote test efficiency. Owing to the mixture of multiple pure states, a single MSTC is more likely to detect a fault than a PSTC, thereby enhancing test effectiveness. This article then proposes a unit testing framework, including generation and execution of MSTCs. Also, this article presents two guidelines and two parameterized quantum circuits to prepare desired mixed states. Empirical studies evaluate the performance of MSTCs and the experimental results demonstrate that MSCTs generally consume less time and detect more faults than PSTCs.

Quantum Computing (QC) leverages quantum mechanics to manipulate quantum information, holding greater potential than classical computing. To fully exploit QC’s potential, it is crucial to ensure the reliability and quality of quantum programs. Research on quantum program testing is still at its early stage, in which some distinctive features of quantum programs, e.g., superposition and entanglement, may be overlooked, and the fault detection capability and testing effectiveness are rather limited. Besides, the input space of quantum programs may exponentially grow when the number of qubits increases, posing great challenges to testing quantum programs. It is imperative to develop a proper testing strategy to effectively select the potential failure-causing test cases and detect faults faster. In this paper, test cases with both basis states and superposition ones are considered and generated to cover more input space. A hybrid distance measurement method based on quantum fidelity and Hamming distance is presented for measuring the similarity among quantum test cases. Furthermore, a Dynamic Random Testing strategy based on Hybrid distance metrics (DRT-H) for quantum programs is proposed, which combines the hybrid distance metrics and the feedback mechanism of the classical Dynamic Random Testing (DRT) strategy to adjust the testing profile and guide the test case selection. Experimental studies demonstrate that the proposed DRT-H strategy outperforms the baseline testing strategies in most cases.

With the continuous advancement of quantum computing (QC), the demand for high-quality quantum programs (QPs) is growing. To avoid program failure, in software engineering, the technology of automatic program repair (APR) employs appropriate patches to remove potential bugs without the intervention of a human. However, the method tailored for repairing defective QPs is still absent. This article proposes, to the best of our knowledge, a new APR method named UnitAR that can repair QPs via unitary operation automatically. Based on the characteristics of superposition and entanglement in QC, the article constructs an algebraic model and adopts a generate-and-validate approach for the repair procedure. Furthermore, the article presents two schemes that can respectively promote the efficiency of generating patches and guarantee the effectiveness of applying patches. For the purpose of evaluating the proposed method, the article selects 29 mutated versions as well as five real-world buggy programs as the objects and introduces two traditional APR approaches GenProg and TBar as baselines. According to the experiments, UnitAR can fix 23 buggy programs, and this method demonstrates the highest efficiency and effectiveness among three APR approaches. Besides, the experimental results further manifest the crucial roles of two constituents involved in the framework of UnitAR .

Deep neural networks (DNNs) have achieved tremendous development while they may encounter with incorrect behaviors and result in economic losses. Identifying the most represented data become critical for revealing incorrect behaviours and improving the quality DNN-driven systems. Various testing strategies for DNNs have been proposed. However, DNN testing is still at early stage and existing strategies might not sufficiently effective. Dynamic random testing (DRT) strategy uses the feedback mechanism to guide the test case selection, which has been proved to be effective in fault detection. However, its efficacy for Natural Language Processing (NLP) DNN models has not been thoroughly studied. In this paper, a Distance-based DRT with prioritization (D-DRT-P) is proposed, which combines the priority information and distance information into DRT to guide the selection of test cases and testing profile adjustment. Empirical studies demonstrate that D-DRT-P can improve the fault detecting effectiveness than other test prioritization strategies in most cases.