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.

One goal of software testing strategies is to detect faults faster. Dynamic Random Testing (DRT) strategy uses the testing results to guide the selection of test cases, which have shown to be effective in the fault detection process. However, the effectiveness of DRT still can be improved. In this paper, a distance-based DRT (D-DRT) strategy is proposed. The vectorized test cases are partitioned with k-means clustering method to obtain better classification, and the distance information are used to guide the test case selection, then the test cases that are close to failure-causing test cases are more likely to be selected, thus the testing process can be optimized. In the case study, the performance of D-DRT and other testing strategies are compared. The experiment results show that the proposed D-DRT strategy has better fault detection effectiveness than the others without significant increase in computational cost.

A particularly good software testing strategy is to achieve the underlying testing goal while solving the problems of tradeoffs between testing effectiveness and efficiency. To improve the fault detection effectiveness of software testing, the principle of feedback control theory was adopted, which motivated the proposal of dynamic random testing (DRT). The main idea behind DRT is using the testing results to guide the test case selection to increase the selection probabilities of the subdomains with higher fault detection rates. Previous works show that DRT strategy can achieve better effectiveness than random testing strategy and random partition testing strategy, and has significantly lower computational costs than adaptive testing strategy. However, the essential factors that affect the performance of DRT, i.e., adjusting parameters, initial profile, and test case classification have not been thoroughly investigated. Besides, some experimental assumptions are inconsistent with real scenarios. Therefore, this paper gives a series of investigations on DRT with a set of practical subject programs. More specifically, the effectiveness and efficiency of DRT are presented, and the extended experiments on DRT with relevant factors are conducted. The results indicate that the effectiveness of DRT is robust to different initial profiles and affected noticeably by the adjusting parameter settings and test case classification methods.