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REVIEW 4 major objections 5 minor 53 references

Quantum-Based Software Engineering

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read This paper argues that quantum algorithms should be aimed at classical software engineering tasks such as test selection, clone detection, and defect prediction, and proposes a research agenda to organize that effort.

desk verdict A competent roadmap paper that re-bundles known quantum-for-SE ideas under the QBSE label; useful as a map, thin on novelty, and a bit overclaiming in the abstract. read the letter →

arxiv 2505.23674 v2 pith:GKJECNTS submitted 2025-05-29 cs.SE quant-ph

Jianjun Zhao This is my paper
classification cs.SEquant-ph
keywords quantumcomputingquantum-basedsoftwareengineeringtestingdefectpredictioncodeclonedetectionQAOAannealingmachinelearning
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Quantum-Based Software Engineering (QBSE) is proposed as a distinct research direction in which quantum computing is used to solve classical software engineering problems rather than to build software for quantum machines. The paper argues that many everyday SE tasks—selecting regression tests, minimizing test suites, detecting code clones, predicting defects, and spotting vulnerabilities—are optimization, search, or classification problems over large spaces, the same shape of problem where Grover's algorithm, QAOA, quantum annealing, and quantum machine learning claim advantages. It organizes currently scattered studies by task and technique, and lays out a research agenda built on reformulating problems into QUBO/Ising or parameterized-circuit form, designing hybrid methods, benchmarking, and tooling. The claim is prospective: the paper establishes a framing and a map, not a demonstration that quantum advantage already exists for these tasks.

What carries the argument

The central working object is the QUBO (quadratic unconstrained binary optimization) formulation, together with its Ising and parameterized-quantum-circuit relatives: a software engineering problem is rewritten as a binary objective whose minimum-energy assignments correspond to good solutions, making it digestible by QAOA, quantum annealers, or variational circuits. The paper's Table 2 then acts as the field's map, pairing each SE task with the quantum technique (Grover search for large solution spaces, QAOA or annealing for test selection and minimization, QNN/QSVM for defect and vulnerability classification) and with the studies that try it. All of the proposed agenda—reformulation, method design, benchmarking, tooling—hangs on the assumption that SE problems can be faithfully expressed in these forms.

What would settle it

Take several publicly available regression test suites of industrial size, encode test case selection as a QUBO, and compare QAOA and quantum annealing with classical integer programming and greedy heuristics on solution quality and total time; if the quantum methods do not at least match the classical baselines at practical sizes, the paper's practical case collapses.

Watch

Extended reading notes

Core claim

The paper's central claim is that QBSE should be recognized as its own direction inside software engineering, with a scope that is the mirror image of quantum software engineering: instead of engineering quantum programs, QBSE applies quantum algorithms to classical SE problems. Its evidence is a survey of early applications—test case optimization via QAOA and annealing, test suite minimization via Grover and QUBO, regression testing via Grover and quantum counting, defect and QoS prediction via QSVM/QNN/QELM, code clone detection via QUBO/QUDO, static analysis via Grover search, vulnerability detection via QNNs, and FSM property checking via Grover—and a mapping of these into a task–technique table. The paper explicitly concedes that most applications are at proof-of-concept stage and that encoding real SE problems into quantum-friendly forms remains open, so the contribution is the framing, the taxonomy, and the agenda rather than empirical results.

Load-bearing premise

The load-bearing premise is that a quantum advantage seen on small or idealized instances will survive when real software engineering problems are encoded for near-term hardware and scaled to realistic codebases.

Editorial extensions

If this is right

  • A recognized QBSE direction would give scattered studies shared terminology, benchmarks, and evaluation criteria, making results comparable.
  • Test case selection, minimization, and regression testing become candidate problems for QAOA, Grover-based search, and annealing, with QUBO formulations as the common interface.
  • Static analysis and FSM property checking could inherit Grover's quadratic speedup for their search-heavy subtasks, though the paper notes such results remain proof-of-concept.
  • Quantum machine learning models become plausible classifiers for defect prediction and vulnerability detection, particularly on small or unbalanced datasets.
  • The proposed agenda treats problem reformulation into QUBO/Ising/PQC as the first step, implying that research effort should concentrate on encodings before hardware experiments.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper leaves implicit that its agenda can be tested immediately on classical simulators: a QUBO encoder for test-suite minimization could be benchmarked against a classical integer programming solver before any quantum hardware is involved.
  • A natural extension the paper does not develop is connecting QBSE to classical metaheuristic search; many SE optimization tasks already have strong classical baselines, so quantum methods must beat those baselines, not just run.
  • If early evidence holds, the same QUBO encodings used for test selection could transfer to other combinatorial SE problems, such as release planning or configuration selection, because the mathematical form is identical.
  • A concrete falsifiable follow-up suggested by the survey: run QSVM and classical SVM on the same defect datasets with identical feature preprocessing and statistical testing, since the current evidence is scattered across different setups.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper introduces Quantum-Based Software Engineering (QBSE) as a research direction distinct from Quantum Software Engineering (QSE), in which quantum algorithms (Grover search, QAOA/VQE, quantum annealing, quantum machine learning) are applied to classical software engineering tasks such as test case optimization, defect prediction, code clone detection, static analysis, vulnerability detection, and specification checking. It provides background on quantum computing, a taxonomy of relevant quantum techniques (Table 1), a mapping of techniques to software engineering tasks with references (Table 2), a review of emerging applications organized by task (Section 6), and a preliminary research agenda (Section 7). No new experiments or derivations are presented; the contribution is conceptual organization, terminology, and agenda-setting.

Significance. As a position and survey paper, its primary value lies in naming and organizing a fragmented literature and in clearly distinguishing QBSE from QSE. The task-technique mapping in Table 2 and the explicit recognition of open problems such as problem reformulation and empirical evaluation are useful starting points for the community. The paper is appropriately hedged in places, notably in Section 6.6, where it concedes that most applications 'remain in the early stages' and are proof-of-concept. However, the central motivating claim that quantum techniques can improve the efficiency and scalability of software engineering tasks is not supported by end-to-end resource analysis, and several supporting references are non-peer-reviewed preprints. The manuscript's contribution is therefore organizational rather than evidential, and its main claims need careful qualification.

major comments (4)
  1. [Section 5.2] The term 'Quadratic Unconstrained Directed Optimization (QUDO)' is introduced as 'a related formulation' extending QUBO, but no citation, definition, or Hamiltonian is provided. The paper later claims in Table 2 and Section 6.3 that code clone detection uses QUBO/QUDO based on Ref. [19]. Either define QUDO with a proper reference or remove the term; as written, a central technique class is presented as established without support.
  2. [Section 5.1, Eq. (1); Sections 6.1 and 6.5] The quadratic speedup of Grover's algorithm is stated as O(sqrt(N)) oracle iterations, and this is transferred to software engineering tasks such as regression testing, FSM property checking, and static analysis. In these applications, the oracle is an expensive classical predicate (e.g., reachability or coverage evaluation), and the paper does not account for the cost of constructing and reversibly executing that oracle. The end-to-end comparison is roughly c*T*sqrt(N) + preparation/measurement overhead versus N*T for a classical scan, so the advantage survives only under conditions on N, c, and overhead that are never argued. The manuscript should explicitly state that the cited speedups are query-complexity results, not end-to-end runtime guarantees, and should temper the corresponding application claims accordingly.
  3. [Abstract and Section 1] The opening sentence states that quantum computing 'has demonstrated the potential to solve computationally intensive problems more efficiently than classical methods.' This overstates the evidence reviewed in the paper itself: Section 6.6 concedes that most QBSE applications 'remain in the early stages' and are proof-of-concept, and Section 7 lists empirical evaluation as an open task. The claim should be qualified to reflect that quantum advantage has been demonstrated only in specific theoretical settings or for particular instances, not as a general capability.
  4. [Section 7, first bullet; Sections 5-6] The agenda correctly lists problem reformulation and suitability analysis as an open task, but the paper does not apply this caveat retroactively to the applications summarized in Sections 5 and 6. Many of those applications already assume faithful QUBO, Ising, or PQC encodings (e.g., test case optimization in [53,54], clone detection in [19]), although the paper itself states that such reformulations remain open. Table 2 should therefore be presented as a mapping of research targets or hypotheses rather than established capabilities, to avoid a circularity between the motivating examples and the open agenda.
minor comments (5)
  1. [Section 3] The sentence 'QBSE is conceptually distinct QSE1' is missing the word 'from' and has a malformed footnote marker; it should read 'QBSE is conceptually distinct from QSE.'
  2. [Table 2] Quantum Counting appears in the table for regression testing, but quantum counting is not described in Section 5. Either add a brief explanation of quantum counting in the background section or remove it from the table.
  3. [References [49], [51]] Reference [49] has a spurious space in the first author name ('W ang'), and Reference [51] contains a mojibake string 'NygØIrd'; these should be corrected.
  4. [Section 6.2] The claim that QSVMs 'may outperform classical SVMs in certain software defect datasets' relies on Ref. [32], which is an arXiv preprint; given the exploratory nature of that work, the text should explicitly flag it as a preliminary result.
  5. [Throughout] Several supporting references are arXiv preprints (e.g., [4,25,32,33,48,50]); the text should consistently mark them as non-peer-reviewed, particularly where they are used to support performance or speedup statements.

Circularity Check

1 steps flagged · score 2.0 of 10

No load-bearing circularity: the paper is a position/survey; the only self-referential element is defining QBSE as the set of quantum-for-SE studies it then organizes, plus minor self-citations.

  1. renaming known result [Section 3 and Section 6.6]
    "QBSE is a research direction that explores how quantum algorithms and hardware can be applied to solve problems in classical software engineering, such as test case selection, static analysis, code clone detection, and defect prediction [36]. [...] Compared with these works, our paper aims to help establish QBSE as a distinct research direction by systematically organizing and interpreting early applications of quantum computing to classical software engineering tasks."

    The defining scope of QBSE is extensionally identical to the body of early applications the paper surveys; the claim that QBSE is a distinct research direction is supported mainly by organizing the studies that already instantiate the label, rather than by deriving a falsifiable consequence. This is a terminological/perspective contribution, not a fitted prediction or an equation-level reduction: no empirical quantity is derived from itself, and the speedup claims are attributed to external citations. The circularity is real but mild.

full rationale

The paper does not fit parameters, execute benchmarks, or derive predictions from its own equations; Sections 5 and 6 summarize external work (e.g., [19], [27], [32], [47], [53], [54], [59]) and the author's prior [40] is used only as an illustrative static-analysis example, while [57] supports the contrast term QSE and is corroborated by [3], [31], [37]. Section 6.6 concedes that most applications are early-stage or proof-of-concept, and Section 7 lists problem reformulation and empirical benchmarking as open agenda items, so no fitted input is renamed as a prediction. The sole self-referential move is the QBSE label itself: its scope is defined as quantum computing applied to classical SE, and then the paper organizes the existing studies that satisfy that definition to 'establish QBSE.' That is a standard positioning maneuver for a survey/position paper and does not make any stated result depend on itself by construction. Given also the minor self-citations, a score of 2 reflects low, non-load-bearing circularity.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The paper introduces no free parameters and no physical entities. Its reasoning depends on four domain assumptions about transferability of quantum advantage, encodability of SE problems, hardware readiness, and reliability of the cited studies. These are reasonable position-paper assumptions but not established by the paper.

assumptions (4)
  • domain assumption Quantum advantages demonstrated on idealized algorithms will transfer to software engineering workloads.
    Sections 1 and 5 use Grover, QAOA, annealing and QML as the basis for QBSE; Section 6.6 admits most applications are proof-of-concept, so transferability is assumed.
  • domain assumption Software engineering problems can be encoded as QUBO, Ising, or parameterized quantum circuits without losing the structure that matters.
    Section 7 lists problem reformulation as an open first step, so the encodability premise is not verified.
  • domain assumption NISQ hardware and simulators can run these methods at practically relevant scale.
    Section 4 motivates timing via hardware improvement; no scale evidence is provided in the paper.
  • domain assumption The cited empirical and preprint studies are valid, representative, and accurately summarized.
    Sections 6.1 through 6.5 build the survey on external results that the paper does not independently evaluate, several of them arXiv preprints.

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Cite this review

Pith. "Pith review of Quantum-Based Software Engineering." pith.science (2026). https://pith.science/paper/GKJECNTS

@misc{pith2026250523674,
  author       = {Pith},
  title        = {Pith review of: Quantum-Based Software Engineering},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GKJECNTS}},
  note         = {Machine review of arXiv:2505.23674}
}
read the original abstract

Quantum computing has demonstrated the potential to solve computationally intensive problems more efficiently than classical methods. Many software engineering tasks, such as test case selection, static analysis, code clone detection, and defect prediction, involve complex optimization, search, or classification, making them candidates for quantum enhancement. In this paper, we introduce Quantum-Based Software Engineering (QBSE) as a new research direction for applying quantum computing to classical software engineering problems. We outline its scope, clarify its distinction from quantum software engineering (QSE), and identify key problem types that may benefit from quantum optimization, search, and learning techniques. We also summarize existing research efforts that remain fragmented. Finally, we outline a preliminary research agenda that may help guide the future development of QBSE, providing a structured and meaningful direction within software engineering.

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.