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REVIEW 4 major objections 6 minor 1 cited by

Quantum Optimization for Software Engineering: A Survey

T0 review · 4 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read This paper presents the first systematic literature review of quantum and quantum-inspired optimization applied to classical software engineering problems, finding 77 primary studies and showing where the field is concentrated and where…

desk verdict A useful first map of quantum(-inspired) optimization for classical SE, but the inconsistent search queries and a few sloppy errors should be fixed before the numbers are relied on. read the letter →

arxiv 2506.16878 v1 pith:66SSS5J2 submitted 2025-06-20 cs.SE

Man Zhang , Yuechen Li , Tao Yue , Kai-Yuan Cai This is my paper · ORCID
classification cs.SE
keywords quantumoptimizationquantum-inspiredalgorithmssoftwareengineeringsystematicliteraturereviewsearch-basedsingle-objectivereformulationQUBOtestsuiteminimization
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

This paper sets out to establish a comprehensive, reproducible map of research that applies quantum or quantum-inspired optimization to classical software engineering problems. It claims to be the first systematic literature review of that specific intersection, screening 2083 candidate publications down to 77 primary studies. The review reports that the literature is concentrated on software engineering operations and software testing, that most optimization problems are reformulated into single-objective form even when the original SE problem had multiple objectives, and that quantum-inspired algorithms running on classical hardware currently outnumber pure quantum solutions. If the map is accurate, it gives the software engineering community a baseline for where quantum optimization already contributes and where it does not.

What carries the argument

The carrying object is the systematic literature review protocol itself: searches across six digital databases using a combined five-set Boolean query, explicit inclusion and exclusion criteria, and two rounds of backward and forward snowballing. This protocol turns 2083 raw hits into 77 primary studies and produces the tables that support every gap claim. A second distinctive device is the classification scheme that distinguishes the original SE problem's objectives from the objectives in the reformulated optimization problem, which lets the paper quantify a simplification tendency: multi-objective and many-objective SE problems are frequently collapsed into single-objective quantum formulations. The same protocol uses a standardized software engineering body of knowledge to label activities, which is what makes the claim about uncovered areas a clean result.

What would settle it

An independent reviewer could rerun the exact queries from the paper's appendix against the same six databases and apply the inclusion criteria; if the deduplicated total does not reproduce near 2083, or if the added studies shift the share of software engineering operations well below 55.84% and fill one of the reportedly empty activity areas, the review's quantitative map would be weakened.

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Extended reading notes

Core claim

The paper's central discovery is the distributional shape of a young interdisciplinary field. It reports that, among 77 primary studies published between 2014 and 2025, 55.84% target software engineering operations, 20.78% target software testing, 12.99% target software quality, and 10.39% target software security; scheduling and test suite minimization are the two most frequently studied problems. It also reports that 65.75% of the SE problems are single-objective as originally posed, but after reformulation 91.67% of the optimization problems are single-objective, and that 48 of 80 solution instances are quantum-inspired approaches executed on classical hardware, with 17 hybrid and 15 purely quantum. Among the 32 approaches that use quantum algorithms, quantum annealing paired with a QUBO formulation dominates, and six of the fifteen activity chapters in the adopted software engineering body of knowledge have no primary study at all. The paper presents this as evidence that the field is real, growing, and still highly uneven.

Load-bearing premise

The review's conclusions stand on the assumption that its literature retrieval was complete and unbiased; if the database queries or the human inclusion and exclusion decisions missed a substantial share of relevant work, the reported percentages and the claimed gaps would no longer represent the field.

Editorial extensions

If this is right

  • The field now has a reproducible baseline map; later reviewers can rerun the published queries and update the counts without starting from zero.
  • Six SE activity areas — architecture, construction, process, models and methods, professional practice, and economics — are essentially open terrain for quantum and quantum-inspired optimization research.
  • Because 91.67% of reformulated problems are single-objective, the trade-off structure of real multi-objective SE problems is largely unexplored; working on multi-objective quantum formulations is a direct research opening.
  • The dominance of quantum annealing with QUBO means that advances in gate-based algorithms or in hybrid classical-quantum decomposition could substantially change the field's center of gravity.
  • Most current solutions are quantum-inspired and run on classical hardware, so near-term practical use does not depend on waiting for fault-tolerant quantum computers.

Reading between the lines

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

  • If the reported distribution is representative, the gap list doubles as a research prioritization menu: testing and operations are crowded, while requirements selection, design, construction, and process improvement are nearly empty.
  • The observation that most primary studies appear outside SE venues implies an indexing problem that the review itself only partly solves; keeping the map current would require a cross-disciplinary watch rather than a single-community search.
  • A testable extension would repeat the protocol at a later date and track whether the share of hybrid and purely quantum solutions grows as hardware becomes more accessible, and whether the six uncovered activities gain studies.
  • The original-versus-reformulated objective count could become a standard reporting metric for future papers in this area, making the community's simplifying choices visible and comparable.
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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 / 6 minor

Summary. This paper presents a systematic literature review (SLR) of research applying quantum and quantum-inspired optimization to classical software engineering (SE) problems. The authors searched six digital databases on 13 January 2025, retrieved 2083 records (2186 raw hits), and after screening and snowballing selected 77 primary studies. They answer 11 research questions covering bibliometric trends, targeted SE activities and problems, optimization reformulations, solution types (quantum, hybrid, quantum-inspired), quantum algorithms used, and open challenges. The key reported findings are that 55.84% of primary studies target 'software engineering operations', that testing/quality/security make up most of the rest, that 91.67% of reformulated problems are single-objective, and that six SWEBOK activities remain uncovered. The authors claim this is the first SLR specifically on quantum and quantum-inspired optimization for classical SE.

Significance. The review addresses a genuinely useful gap: the SBSE community currently lacks a systematic map of how quantum and quantum-inspired optimization have been applied to classical SE problems. If the corpus is representative, the paper's maps (venues, activities, problems, solution types, algorithms) provide a baseline for future research and for the QSE roadmap. Strengths include the public artifact (GitHub) with extracted data, the detailed search and snowballing protocol, the use of SWEBOK v4.0 as an organizing framework, and the explicit RQ-by-RQ findings with reproducible figures and tables. The main value is the empirical mapping rather than a new technique. My reservations concern internal inconsistencies in the reported findings and the reproducibility of the corpus construction; these are correctable but must be addressed before the paper's claims can be relied upon.

major comments (4)
  1. [§7.2.1 (Findings of RQ11)] The finding states that 'six out of 15 SE activities have been covered by the primary studies, i.e., Software Architecture, Software Construction, SE Process, SE Models and Methods, SE Professional Practice, and SE Economics.' Table 11 marks exactly these six activities as ✗ (not covered), and the body text (e.g., 'there is no primary study that targets software architecture optimization') confirms they are absent. The RQ11 finding is thus the opposite of the evidence it summarizes. Please correct the finding to 'not covered' and ensure the count (6 of 15 uncovered, 9 covered) is stated consistently.
  2. [§5.2 (paragraph after Table 5)] The text says: 'For software failure prediction (see ▶ in Table 5), all seven studies are categorized under software security.' In Table 5, software failure prediction appears under 'software quality' (7 papers), not under 'software security'; the overlap marker ▶ indicates it is shared between software quality and software engineering operations (paper [91]). The sentence should say 'software quality'. As written, the paragraph misreports the paper's own classification and confuses the subsequent discussion of the overlap.
  3. [§3.2 / Appendix Table 14] The six database queries are not semantically equivalent instantiations of Listing 1. For the technique set (Set 3), Springer uses 'artificial intelligence OR ai', whereas Wiley and Web of Science use 'artificial AND intelligence OR ai', which parse differently under standard precedence. The field scope for 'software' also varies: full text (IEEE, ACM), ALL fields (Scopus, WoS), Abstract (Wiley), and no field restriction (Springer). These differences mean the 2083-retrieved pool is not the output of a single reproducible Boolean query, and the subsequent screening and snowballing cannot fully repair a systematic gap in the seed set. Since the paper's central empirical claims (e.g., the 55.84% share for SE operations, the six uncovered activities) are computed from this corpus, the authors should either show that the query variants are equivalent after operator and field semantics are accounted for, or re-run the search with normalized queries (and report the new corpus) to demonstrate the stability of the reported distributions.
  4. [§6.3 vs Table 7] The text states 'among the 17 hybrid quantum optimization solutions and 15 purely quantum optimization solutions', but Table 7 reports Hybrid 16 and Quantum 15. The discrepancy affects the interpretation of RQ9 counts (e.g., Figure 12 shows 32 quantum-approach instances). Please reconcile the count and state clearly how papers with multiple approaches (e.g., [20], [105], [130]) are counted.
minor comments (6)
  1. [References] References [1] and [2] are identical (Abbas et al., 2024), as are [102] and [103] (Murillo et al., 2025). Please remove duplicates or renumber.
  2. [§4.3 (Findings of RQ3)] The sentence 'surpassing 27.27% in conferences, 10.39% in workshops, and 3 in open-access archive entries' mixes a count with percentages; state '3 papers (3.90%)' for consistency.
  3. [§1 (Introduction)] The word 'reproducability' should be 'reproducibility'.
  4. [§7.2.2] The phrase 'were each quantum solution is associated' should be 'where each quantum solution is associated'.
  5. [§1 & Abstract] The claim 'we present the first SLR' should be qualified relative to Mandal et al. [96] and Murillo et al. [103], which are also 2025 SLRs with partial overlap; the paper already discusses the differences in scope in Section 9.1, but the abstract and intro should be worded to avoid an overclaim.
  6. [§3.3 / Table 14] Table 14 sums to 2186 raw hits, while the text reports 2083 unique papers after deduplication. Please clarify explicitly whether the 2083 number already excludes duplicates from the six-source union, as the current wording in Section 3.3 ('yielding 2083 papers') is ambiguous.

Circularity Check

1 steps flagged · score 6.0 of 10

RQ11's 'uncovered SWEBOK activities' gap is largely an artifact of the Set 4 search-term list, making a headline finding partly self-definitional.

  1. self definitional [Section 3.2 (Listing 1 / Table 14) and Section 7.2.1 (Table 11)]
    "Section 3.2: 'The terms in Set 4 collectively covers core SE phases or activities referred to another recent SLR [89]'. Table 14: 'api OR develop* OR bug OR code OR coding OR debug OR defect OR deploy OR evolution OR fault OR fix OR maintenance OR program OR refactor* OR repair OR requirement OR test* OR verification OR validation OR vulnerab* OR configur*'."

    Set 4 is a closed keyword list of SE activities. The SWEBOK chapters reported as uncovered—Software Architecture, Construction, Process, Models/Methods, Professional Practice, Economics—have no representative keyword in Set 4 (no 'architecture', 'design', 'construction', 'process', 'model*', 'method*', 'professional', or 'economics'). A paper addressing only those activities cannot pass the mandatory Set 4 filter, so its absence from the 77-study corpus is a property of the search string, not an empirically established gap. The paper presents this absence as RQ11's research gap and even rationalizes it as 'meta-level concerns', without noting that the query never searched those activities. The gap claim is thus partly equivalent to the inclusion filter by construction.

full rationale

The SLR's main corpus construction is externally grounded: 2083 records from six databases were screened and snowballed to 77 primary studies, and the bibliometric/solution-type distributions (RQs 1-3, 6-10) are empirical classifications of those studies, not fitted predictions. Self-citations such as [86], [87], [150], [151], [116], and [163] are published, externally available results and are not load-bearing for the search-and-classification method. The one material circularity is the RQ11 gap analysis: the search query's Set 4 term list omits several SWEBOK activity areas, and the paper's headline 'significant gaps across other SE activities' is at least partly a consequence of that omission rather than an independent discovery. This does not invalidate the rest of the survey, but the gap claims should be re-stated as 'not retrieved by our query' or the query should be re-run with a SWEBOK-derived term set. Accordingly, the circularity is partial rather than total.

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

The survey's findings rest on corpus completeness and classification frame; no free parameters or invented entities are introduced. The categories (e.g., 'Circuit-based' vs 'Physics-based' inspiration mechanisms) are classifications of existing literature, not new postulated entities.

assumptions (3)
  • domain assumption The six selected digital libraries (ACM, IEEE Xplore, Scopus, Springer, Web of Science, Wiley) plus manual snowballing are sufficient to retrieve the relevant literature corpus.
    Stated in Section 3.2 and used to justify the 2083-paper pool; if the corpus is incomplete, the gap analysis is biased.
  • domain assumption SWEBOK v4.0 provides a valid and complete classification of software engineering activities for mapping primary studies.
    Used in Section 5.1 and 7.2 to define SE activities and to claim that certain activities are uncovered; alternative taxonomies could shift the conclusions.
  • domain assumption The inclusion/exclusion criteria in Table 2 can be applied consistently across 2083 papers by human reviewers.
    The selection is performed subjectively by the authors; inconsistency would alter the primary study set and downstream statistics.

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Cite this review

Pith. "Pith review of Quantum Optimization for Software Engineering: A Survey." pith.science (2026). https://pith.science/paper/66SSS5J2

@misc{pith2026250616878,
  author       = {Pith},
  title        = {Pith review of: Quantum Optimization for Software Engineering: A Survey},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/66SSS5J2}},
  note         = {Machine review of arXiv:2506.16878}
}
read the original abstract

Quantum computing, particularly in the area of quantum optimization, is steadily progressing toward practical applications, supported by an expanding range of hardware platforms and simulators. While Software Engineering (SE) optimization has a strong foundation, which is exemplified by the active Search-Based Software Engineering (SBSE) community and numerous classical optimization methods, the growing complexity of modern software systems and their engineering processes demands innovative solutions. This Systematic Literature Review (SLR) focuses specifically on studying the literature that applies quantum or quantum-inspired algorithms to solve classical SE optimization problems. We examine 77 primary studies selected from an initial pool of 2083 publications obtained through systematic searches of six digital databases using carefully crafted search strings. Our findings reveal concentrated research efforts in areas such as SE operations and software testing, while exposing significant gaps across other SE activities. Additionally, the SLR uncovers relevant works published outside traditional SE venues, underscoring the necessity of this comprehensive review. Overall, our study provides a broad overview of the research landscape, empowering the SBSE community to leverage quantum advancements in addressing next-generation SE challenges.

Figures

Figures reproduced from arXiv: 2506.16878 by the authors.

Figure 1
Figure 1. The framework of the adopted methodology for collecting and filtering primary studies [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 2
Figure 2. Number of publications over the years (2014-2025) [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
Figure 3
Figure 3. Number of publications per venue type 10 [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: Number of primary studies belongs to SE or Non-SE [PITH_FULL_IMAGE:figures/full_fig_p011_4.png]
Figure 5
Figure 5. Figure 5: Bar plot representing the distribution of papers across SE activities [PITH_FULL_IMAGE:figures/full_fig_p012_5.png]
Figure 6
Figure 6. Figure 6: Bar plot representing the distribution of papers for SE problem [PITH_FULL_IMAGE:figures/full_fig_p012_6.png]
Figure 7
Figure 7. Figure 7: Number of papers associated with Single, Multiple and Many objectives for SE problems an increasing convergence of development, deployment, and runtime monitoring to systematically ensure security and quality across the SDLC of software systems. Findings of RQ5 In tota…
Figure 8
Figure 8. Figure 8: Number of papers associated with Single, Multiple and Many objectives in reformulation of SE problems N 29 (39.19%) Y 45 (60.81%) [PITH_FULL_IMAGE:figures/full_fig_p016_8.png]
Figure 9
Figure 9. Figure 9: Number of proposed optimizations with defined constraints [PITH_FULL_IMAGE:figures/full_fig_p016_9.png]
Figure 10
Figure 10. Figure 10: Bar plot representing the distribution of papers associated with each type of quantum or quantum [PITH_FULL_IMAGE:figures/full_fig_p017_10.png]
Figure 11
Figure 11. Figure 11: Number and proportion of quantum-inspired solutions by types of inspiration mechanisms [PITH_FULL_IMAGE:figures/full_fig_p018_11.png]
Figure 12
Figure 12. Figure 12: Frequency of quantum-related techniques employed in quantum approaches [PITH_FULL_IMAGE:figures/full_fig_p020_12.png]
Figure 13
Figure 13. Figure 13: Types of problems (x-axis) addressed by each kind of quantum solutions (y-axis) [PITH_FULL_IMAGE:figures/full_fig_p025_13.png]

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Cited by 1 Pith paper

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

  1. Shadow-Based Noise Fingerprinting of Simulated Quantum Noise Models

    cs.SE 2026-07 conditional novelty 6.0 of 10

    Physics-informed classical-shadow features from fixed 3-qubit probes let a random forest identify ten simulated quantum noise channels at 0.84 test accuracy.

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.