Generating Failing Test Suites for Quantum Programs With Search

Wang, Xinyi; Arcaini, Paolo; Yue, Tao; Ali, Shaukat

doi:10.1007/978-3-030-88106-1_2

Cited by 28 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A problem related to that addressed here is how to test quantum programs. Several approaches have been proposed, including a search-based techniques [30,33], statistical assertion checks that try to limit the effects on the actual computation [14,18,19], combinatorial testing [32], and coverage-based methods [2]. In contrast to our work, these techniques test specific programs, not the platform that programs are running on.…”

Section: Related Workmentioning

confidence: 99%

MorphQ: Metamorphic Testing of the Qiskit Quantum Computing Platform

Paltenghi¹,

Pradel²

2022

Preprint

View full text Add to dashboard Cite

As quantum computing is becoming increasingly popular, the underlying quantum computing platforms are growing both in ability and complexity. This growth may cause bugs in the platforms, which hinders the adoption of quantum computing. Unfortunately, testing quantum computing platforms is challenging due to the relatively small number of existing quantum programs and because of the oracle problem, i.e., a lack of specifications of the expected behavior of programs. This paper presents MorphQ, the first metamorphic testing approach for quantum computing platforms. Our two key contributions are (i) a program generator that creates a large and diverse set of valid (i.e., non-crashing) quantum programs, and (ii) set of program transformations that exploit quantum-specific metamorphic relationships to alleviate the oracle problem. Evaluating the approach by testing the popular Qiskit platform shows that the approach creates over 50k program pairs within two days, many of which expose crashes. Inspecting the crashes, we find twelve bugs, eight of which have already been confirmed. MorphQ widens the slim portfolio of testing techniques of quantum computing platforms, helping to create a reliable software stack for this increasingly important field.

show abstract

Section: Related Workmentioning

confidence: 99%

MorphQ: Metamorphic Testing of the Qiskit Quantum Computing Platform

Paltenghi¹,

Pradel²

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Tracking the evolution of traditional software programming, which began with a strong focus on hardware in the 1950s and later shifted to today's agile practices (Moguel et al 2020;Piattini et al 2021), opens the door for the novel QSE field to adopt traditional agile methods over time. However, given the unique challenges of quantum software development, such as qubit entanglement and superposition, it is increasingly compelling to examine the suitability of traditional agile approaches in line with the characteristics of quantum software development (Stefano et al 2022;Wang et al 2021Wang et al , 2022. For example, composing, designing, testing, and maintaining quantum programs would be challenging due to switching to an entirely different mindset, examining the superposition states of quantum variables, and interpreting multi-dimensional quantum states.…”

Section: Introductionmentioning

confidence: 99%

Agile meets quantum: a novel genetic algorithm model for predicting the success of quantum software development project

Khan,

Akbar,

Lahtinen

et al. 2024

Autom Softw Eng

View full text Add to dashboard Cite

Quantum software systems represent a new realm in software engineering, utilizing quantum bits (Qubits) and quantum gates (Qgates) to solve the complex problems more efficiently than classical counterparts. Agile software development approaches are considered to address many inherent challenges in quantum software development, but their effective integration remains unexplored. This study investigates key causes of challenges that could hinders the adoption of traditional agile approaches in quantum software projects and develop an Agile-Quantum Software Project Success Prediction Model (AQSSPM). Firstly, we identified 19 causes of challenging factors discussed in our previous study, which are potentially impacting agile-quantum project success. Secondly, a survey was conducted to collect expert opinions on these causes and applied Genetic Algorithm (GA) with Naive Bayes Classifier (NBC) and Logistic Regression (LR) to develop the AQSSPM. Utilizing GA with NBC, project success probability improved from 53.17 to 99.68%, with cost reductions from 0.463 to 0.403%. Similarly, GA with LR increased success rates from 55.52 to 98.99%, and costs decreased from 0.496 to 0.409% after 100 iterations. Both methods result showed a strong positive correlation (rs = 0.955) in causes ranking, with no significant difference between them (t = 1.195, p = 0.240 > 0.05). The AQSSPM highlights critical focus areas for efficiently and successfully implementing agile-quantum projects considering the cost factor of a particular project.

show abstract