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REVIEW 4 major objections 5 minor 2 cited by

Quantum Software Engineering and Potential of Quantum Computing in Software Engineering Research: A Review

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

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Pith's one-line read The paper argues that quantum machine learning and quantum optimization are the most promising ways quantum computing could help software engineering, but that the tools of quantum software engineering are not yet mature enough to deliver.

desk verdict A useful map of a young field, but the 'first comprehensive review' claim and the unreproducible search are the real problems; the qualitative conclusions hold up. read the letter →

arxiv 2502.08925 v1 pith:RIHDUART submitted 2025-02-13 cs.SE

Ashis Kumar Mandal , Md Nadim , Chanchal K. Roy , Banani Roy , Kevin A. Schneider This is my paper · ORCID
classification cs.SE
keywords SoftwareengineeringQuantumcomputingmachinelearningalgorithmsoptimizationdefectpredictiontesting
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

The paper tries to establish a map of an emerging intersection: where quantum computing can help software engineering, and how software engineering is being reshaped for quantum software. Running a systematic search across six databases and key conferences, it identifies 56 studies in three groups: quantum machine learning for software engineering tasks, quantum optimization and search for such tasks, and tools and techniques of quantum software engineering (QSE). Its central conclusions are that quantum machine learning and quantum optimization have real but largely untested potential for classical software engineering problems, and that current QSE tools are too immature to support production quantum software. A sympathetic reader would care because the review identifies where a quantum speedup might actually pay off in software engineering and what must be built before that can happen.

What carries the argument

The load-bearing apparatus is the systematic review protocol itself: three search groups with explicit query strings, a set of six academic databases, manual scanning of five quantum and software-engineering conferences and workshops, and inclusion or exclusion criteria that collectively produce the 56-study sample. Within that sample, the paper's substantive machinery consists of the QML and quantum optimization formalisms it surveys—variational quantum classifiers with feature maps and trial quantum states (ansatze), QUBO and Ising encodings for quantum annealing, QAOA and VQE hybrid loops, and Grover-style amplitude amplification—plus generic workflows that map a software engineering problem to one of these quantum approaches. On the QSE side, the machinery is the eight-phase quantum software development lifecycle and a proposed Q-UML modeling extension that marks quantum classes, operations, and communication lines in standard UML diagrams. These tools carry the argument by connecting the small number of application studies to a plausible, reusable path for future research.

What would settle it

An independent re-execution of the paper's documented search protocol that screens the same six databases and conferences and reports every record at each stage would either reproduce the 56-study sample or reveal additional eligible studies; if a re-run finds, say, a dozen more quantum-machine-learning-for-software-engineering papers, the claim that this area is 'still limited' and the 'first comprehensive review' position are directly contradicted.

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Extended reading notes

Core claim

The paper's central claim is twofold. First, QML (quantum neural networks, quantum support vector machines, variational quantum classifiers) and quantum optimization or search algorithms (Grover search, QAOA, VQE, quantum annealing) can in principle address classical software engineering tasks—software defect and vulnerability prediction, software supply chain attack detection, test suite minimization, code clone detection, and SAT-based verification—but the number of studies actually testing this is tiny: roughly 4 in the QML group and 6 in the optimization group. Second, quantum software engineering is emerging as its own discipline, with the bulk of current tools concentrated in quantum testing and debugging, and with formal methods, modeling, refactoring, and reverse engineering beginning to appear; nearly all of these tools are at an early stage and lack the robustness and maturity of classical software engineering tooling. The paper also reports a case study in which a quantum random forest outperforms a classical random forest for bug prediction when datasets are small (100–200 instances) but loses that advantage once data grows, suggesting the benefit of quantum machine learning in software engineering is data-regime-dependent. It positions itself as, to the authors' knowledge, the first comprehensive review of this intersection.

Load-bearing premise

The review's conclusions stand only if its search using three fixed query groups, six databases, and manual conference scanning actually captured the relevant literature, since the paper excludes preprints, theses, and technical reports and does not list the included studies.

Editorial extensions

If this is right

  • If the review's map is correct, the near-term quantum payoff in software engineering is most likely in prediction tasks (defect, vulnerability, supply-chain risk) and in combinatorial problems (test minimization, clone detection, SAT), not in general-purpose software development.
  • The tiny count of QML-for-SE and quantum-optimization-for-SE studies means these areas are open research fronts with room for first results.
  • QSE tooling's concentration in testing and debugging implies that requirements, architecture, maintenance, and refactoring for quantum software are the under-served phases most likely to bottleneck future development.
  • The case study's data-size crossover suggests that quantum machine learning for software engineering should be evaluated against dataset size explicitly, since the advantage may appear only in small-data regimes.

Reading between the lines

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

  • A natural extension the paper leaves implicit: the three-group classification probably undercounts work that uses different vocabulary, such as 'quantum-inspired algorithms' or 'annealing-based test generation,' so the 56-study count is best read as a lower bound on activity.
  • Because the review excludes preprints, theses, and technical reports, the most recent and most speculative quantum software engineering work is likely missing; tracking those sources would show whether tool maturity is advancing faster than the peer-reviewed record suggests.
  • The QRF-over-RF crossover in the case study is a testable hypothesis: if other QML models show the same small-data advantage on more benchmarks, then quantum machine learning's role in software engineering may be specifically as a data-scarce bootstrap, not a general replacement for classical models.
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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. This paper is a literature review of quantum computing applied to software engineering (SE), organized around four research questions: QML for SE tasks, quantum optimization/search for SE tasks, the current state of quantum software engineering (QSE) tools and techniques, and challenges/opportunities. It provides a background on quantum computing, gates, SDKs, and algorithms; reviews a claimed sample of 56 publications in three groups; proposes generic workflows for applying QML and quantum optimization to SE problems; and presents two case studies, one on quantum random forest vs. random forest for software defect prediction and one on Q-UML modeling. The paper concludes that QML and quantum optimization have substantial but largely unrealized potential for classical SE tasks, and that current QSE tooling is immature and needs more robust support.

Significance. If the review's sample is complete and reproducible, the paper would be a useful map of an emerging interdisciplinary area: it organizes a scattered literature into three groups, tabulates QSE tools and their limitations in Table 4, offers a concrete research agenda in Table 5, and presents an empirical case study with a falsifiable comparison (RF vs. QRF on PROMISE data). The paper is also candid in Section 8 about excluding grey literature. However, the central 'first comprehensive review' claim and the field-level conclusions about the area being 'still limited' rest entirely on the completeness and transparency of the Section 3 search. That search is currently not reproducible, and the case study has a data-leakage risk; these issues are load-bearing for the paper's headline claims.

major comments (4)
  1. [Section 3] The search procedure as written cannot reproduce the 56-publication sample. The Group 3 query contains a comma where a Boolean operator is needed ('... "Reverse engineering", "Quantum UML"') and repeats 'Quantum Testing and debugging'; the Group 2 query has a trailing space inside the quoted term 'code clone '. These strings cannot be executed verbatim on IEEE Xplore, ScienceDirect, Springer Link, ACM DL, Scopus, and Web of Science as claimed. The screening counts are also opaque: the text moves from 'approximately 12/17/98' to 'approximately 4/6/43' with no per-stage exclusion counts or PRISMA-style flow, and the final group counts do not sum to the stated total (4+6+43=53, not 56). No list of included studies or per-group mapping is provided, so the abstract's 'first comprehensive review' claim and the conclusion that QML/quantum-optimization work in SE is 'still limited' cannot be verified by a reader.
  2. [Section 4.3] The defect-prediction case study applies SMOTE to each full dataset before any train/test split is described, stating that SMOTE is applied 'before implementing the RF and QRF algorithms' and then partitioning the data within the workflow. This creates a data-leakage risk: synthetic minority instances generated from the full data can appear in both training and validation partitions, which can inflate the reported TP/F1 advantage of QRF. The iteration procedure is also governed by arbitrary free parameters (dataset sizes capped at 500 instances, increments of 100), and no repeated runs, standard deviations, or statistical tests are reported. Consequently, the claims in Figures 18 and 21 that QRF 'outperformed RF in six out of ten projects' in data-scarce settings and that a 'consistent trend' holds across dataset sizes are not statistically established.
  3. [Abstract and Section 1] The novelty claim 'To our knowledge, this is the first comprehensive review on this topic' is stated without the scope qualifications needed to make it defensible, and it is in tension with surveys the paper itself cites (Dwivedi et al. 2024; Serrano et al. 2022; Houssein et al. 2022). If the intended contribution is a review of quantum computing applied to classical software engineering tasks, rather than a review of QSE as a discipline, the abstract should state that narrower scope explicitly; otherwise the novelty claim should be removed or replaced with a precise statement of how this review differs from the cited surveys.
  4. [Section 3 and Reference List] The declared search window is 2004-2023, yet the reference list includes several 2024 publications (e.g., Chen et al. 2024; Dwivedi et al. 2024; Srikumar et al. 2024; Mandal et al. 2024; Volya and Mishra 2024). The paper does not state whether these were part of the systematic search or added after it. This ambiguity further undermines reproducibility and should be clarified, either by extending the search window and reporting the updated counts or by explicitly labeling post-search additions.
minor comments (5)
  1. [Table 1] The Pauli X row contains the typo 'Rsotation gate' and should read 'Rotation gate'; in the T-gate row, '4π/4 (45 degrees)' should read 'π/4 (45 degrees)'.
  2. [Section 2.2] The sentence 'An qubits register can represent a superposition of 2n classical states' should be 'An n-qubit register can represent a superposition of 2^n classical states'.
  3. [Section 5.1] 'In QUML, quantum information can be represented...' should read 'In Q-UML, ...' for consistency with the notation used throughout the section.
  4. [Section 4.2.1] Figure numbering is not sequential: the text refers to 'Figure 16 lists popular quantum optimization approaches' before Figures 13-15 are introduced, making it harder for the reader to follow the cross-references.
  5. [References] The reference entry 'Wang, Xinyi a2022nd Arcaini, P.' is malformed; it should be formatted consistently with the other entries, e.g., 'Wang, X., P. Arcaini, T. Yue, and S. Ali'.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the review's conclusions rest on external literature and an empirical case study, not on fitted inputs or self-citation chains.

full rationale

The paper is a literature review whose central claims (QML and quantum optimization are promising but still limited in software engineering; QSE tools are immature) are supported by summaries of externally published studies and by a case study comparing classical Random Forest with an externally published Quantum Random Forest (Srikumar et al., 2024) on PROMISE datasets. No parameter is fitted to the data it then predicts; the generic workflows in Figures 12 and 17 are illustrative guidelines rather than derived predictions. The only self-references are future-work pointers (Nadim et al., 2025; Mandal et al., 2024) and are not load-bearing. The threats-to-validity section concedes the search excluded preprints, theses, and technical reports, which is a completeness limitation, not circularity. The 'first comprehensive review' novelty claim is a correctness and verifiability concern, but it is not equivalent to the review's inputs by construction. The malformed query strings and absent study list undermine reproducibility, but they do not make any conclusion reduce to an input by definition. Hence no circular step can be quoted and scored.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

No new physical entities, forces, particles, or conserved quantities are introduced. The ledger is dominated by methodological choices in the case study and by background assumptions taken from the cited quantum computing literature.

free parameters (3)
  • Maximum dataset size per project = 500 instances
    Set in Section 4.3 to cap QRF runtime; all comparisons and the crossover conclusion are confined to 100 to 500 rows.
  • Dataset size increment = 100 instances
    Iterations from 100 to 500 in steps of 100 chosen in Section 4.3; this grid shapes the observed small-data advantage and the crossover claim.
  • SMOTE oversampling configuration = unspecified
    Applied in Section 4.3 before training; parameters are not reported and applying it before the split risks leakage, so the comparison is not fully specified.
assumptions (3)
  • domain assumption Quantum algorithms such as Grover, QAOA, and quantum annealing provide the speedups attributed to them in Sections 2.4 and 4.2.
    The review does not measure these speedups; it relies on cited theoretical results.
  • domain assumption The search protocol in Section 3 captures the relevant quantum computing and software engineering literature.
    Required for the 'first comprehensive review' claim; the authors themselves note limitations in Section 8.
  • domain assumption The RF versus QRF comparison on PROMISE data is a fair test of quantum advantage.
    Section 4.3 uses SMOTE, a 500-row cap, and no statistical tests, so this fairness is assumed rather than demonstrated.

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Cite this review

Pith. "Pith review of Quantum Software Engineering and Potential of Quantum Computing in Software Engineering Research: A Review." pith.science (2026). https://pith.science/paper/RIHDUART

@misc{pith2026250208925,
  author       = {Pith},
  title        = {Pith review of: Quantum Software Engineering and Potential of Quantum Computing in Software Engineering Research: A Review},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RIHDUART}},
  note         = {Machine review of arXiv:2502.08925}
}
read the original abstract

Research in software engineering is essential for improving development practices, leading to reliable and secure software. Leveraging the principles of quantum physics, quantum computing has emerged as a new computational paradigm that offers significant advantages over classical computing. As quantum computing progresses rapidly, its potential applications across various fields are becoming apparent. In software engineering, many tasks involve complex computations where quantum computers can greatly speed up the development process, leading to faster and more efficient solutions. With the growing use of quantum-based applications in different fields, quantum software engineering (QSE) has emerged as a discipline focused on designing, developing, and optimizing quantum software for diverse applications. This paper aims to review the role of quantum computing in software engineering research and the latest developments in QSE. To our knowledge, this is the first comprehensive review on this topic. We begin by introducing quantum computing, exploring its fundamental concepts, and discussing its potential applications in software engineering. We also examine various QSE techniques that expedite software development. Finally, we discuss the opportunities and challenges in quantum-driven software engineering and QSE. Our study reveals that quantum machine learning (QML) and quantum optimization have substantial potential to address classical software engineering tasks, though this area is still limited. Current QSE tools and techniques lack robustness and maturity, indicating a need for more focus. One of the main challenges is that quantum computing has yet to reach its full potential.

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Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Quantum-Informed Contrastive Learning with Dynamic Mixup Augmentation for Class-Imbalanced Expert Systems

    cs.LG 2025-06 reject novelty 4.0 of 10

    QCL-MixNet, a hybrid of gated sinusoidal feature scaling, kNN-guided mixup, and contrastive/focal losses, is presented as a new benchmark for imbalanced tabular classification, but the experimental evidence is inconsi...

  2. Quantum-Based Software Engineering

    cs.SE 2025-05 unverdicted novelty 3.0 of 10

    The paper names and scopes QBSE as applying quantum computing to classical software engineering tasks, distinguishes it from quantum software engineering, surveys early results, and proposes an agenda.

Reference graph

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Pith tools

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