Automated Test Oracles

Pezzè, Mauro; Zhang, Cheng

doi:10.1016/b978-0-12-800160-8.00001-2

Cited by 39 publications

(32 citation statements)

References 69 publications

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“…4e). For t < 0.5 τ , the measured gain G remains close to the usual gainḠ deduced from the first two moments, as expected in this regime of gaussian quantum fluctuations [48]. For longer times, the measured gain G largely exceeds the gainḠ, reaching an almost constant value G = 8.6(6) in the oversqueezed regime (average value of t > τ data).…”

supporting

confidence: 80%

“…The best magnetic sensitivity is achieved for a rotation axiŝ b coinciding with the directionn max of maximal spin projection variance ∆J max , and around θ = π/2. We quantify the increase of sensitivity with respect to the SQL by the metrological gainḠ ≡ (∆ν SQL /∆ν) 2 [48]. For durations 0 < t < τ we observe a quantum enhance-mentḠ > 1, with a maximum gainḠ = 4.3(4) reached for t = 0.58(2)τ .…”

mentioning

confidence: 98%

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Enhanced Magnetic Sensitivity with Non-Gaussian Quantum Fluctuations

et al. 2019

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The precision of a quantum sensor can overcome its classical counterpart when its constituents are entangled. In gaussian squeezed states, quantum correlations lead to a reduction of the quantum projection noise below the shot noise limit. However, the most sensitive states involve complex non-gaussian quantum fluctuations, making the required measurement protocol challenging. Here we measure the sensitivity of non-classical states of the electronic spin J = 8 of dysprosium atoms, created using light-induced non-linear spin coupling. Magnetic sublevel resolution enables us to reach the optimal sensitivity of non-gaussian (oversqueezed) states, well above the capability of squeezed states and about half the Heisenberg limit. arXiv:1901.06282v2 [cond-mat.quant-gas]

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confidence: 80%

mentioning

confidence: 98%

Enhanced Magnetic Sensitivity with Non-Gaussian Quantum Fluctuations

et al. 2019

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“…Since the usage of service oriented architectures began, service testing automation has been a critical challenge for researchers and practitioners [27][33] [86] [88]. In particular, tasks such as: (i) the optimised generation of test inputs [27], (ii) the generation of test oracles [19][24] [74], and (iii) the optimised management of test suites for different test activities -such as progression testing, change testing, regression testing [88] have not yet found automation solutions that can be applied to real complex service architectures such as those that are implemented in the healthcare industry [33].…”

Section: Related Workmentioning

confidence: 99%

Automation and intelligent scheduling of distributed system functional testing

Hillah

Maesano

Rosa³

et al. 2016

Int J Softw Tools Technol Transfer

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This paper presents the approach to functional test automation of services (black-box testing) and service architectures (grey-box testing) that has been developed within the MIDAS project and is accessible on the MIDAS SaaS. In particular, the algorithms and techniques adopted for addressing input and oracle generation, dynamic scheduling, and session planning issues supporting service functional test automation are illustrated. More specifically, the paper details: (i) the test input generation based on formal methods and temporal logic specifications, (ii) the test oracle generation based on service formal specifications, (iii) the dynamic scheduling of test cases based on probabilistic graphical reasoning, and (iv) the reactive, evidence-based planning of test sessions with on the fly generation of new test cases. Finally, the utilisation of the MIDAS prototype for the functional test of operational services and service architectures in the healthcare industry is reported and assessed. A planned evolution of the technology deals with the testing and troubleshooting of distributed systems that integrate connected objects (IoT).

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“…Thus, it could be necessary to translate inputs that are used by the SUT into a form that is compatible with the ML algorithm and to translate outputs into a form that is amenable for comparison with the SUT's output (Pezzè and Zhang 2014). If such translations are necessary, the developers of ML Oracles must either write additional programs to automate this translation task, or perform the translation task manually.…”

Section: Design and Application Of Machine Learning Oraclesmentioning

confidence: 99%

“…Finally, some systematic reviews have additional or different objectives. For example, Pezzè and Zhang (2014) and Oliveira et al (2014a) endeavoured to establish a taxonomy to classify oracles and Harman et al (2013) examined trends in research on the oracle problem.…”

Section: Requires Domain Knowledgementioning

confidence: 99%

A mapping study on testing non-testable systems

Patel

Hierons

2017

Software Qual J

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The terms "Oracle Problem" and "Non-testable system" interchangeably refer to programs in which the application of test oracles is infeasible. Test oracles are an integral part of conventional testing techniques; thus, such techniques are inoperable in these programs. The prevalence of the oracle problem has inspired the research community to develop several automated testing techniques that can detect functional software faults in such programs. These techniques include N-Version testing, Metamorphic Testing, Assertions, Machine Learning Oracles, and Statistical Hypothesis Testing. This paper presents a Mapping Study that covers these techniques. The Mapping Study presents a series of discussions about each technique, from different perspectives, e.g. effectiveness, efficiency, and usability. It also presents a comparative analysis of these techniques in terms of these perspectives. Finally, potential research opportunities within the non-testable systems problem domain are highlighted within the Mapping Study. We believe that the aforementioned discussions and comparative analysis will be invaluable for new researchers that are attempting to familiarise themselves with the field, and be a useful resource for practitioners that are in the process of selecting an appropriate technique for their context, or deciding how to apply their selected technique. We also believe that our own insights, which are embedded throughout these discussions and the comparative analysis, will be useful for researchers that are already accustomed to the field. It is our hope that the potential research opportunities that have been highlighted by the Mapping Study will steer the direction of future research endeavours.

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Automated Test Oracles

Cited by 39 publications

References 69 publications

Enhanced Magnetic Sensitivity with Non-Gaussian Quantum Fluctuations

Enhanced Magnetic Sensitivity with Non-Gaussian Quantum Fluctuations

Automation and intelligent scheduling of distributed system functional testing

A mapping study on testing non-testable systems

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