{"id":"513fbeb9-60bb-4289-a6a9-c18572488948","arxiv_id":"2506.07135","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":5.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"LLMs can generate a structured taxonomy of Qiskit migration and refactoring scenarios that largely overlaps with an expert-built taxonomy and adds some scenarios.","lead":"This paper builds a taxonomy of code changes needed to migrate quantum programs between versions of Qiskit, using both human experts and large language models, and compares the two. It reports that LLMs can generate similarly structured taxonomies and sometimes find scenarios experts missed, which could speed up migration tooling for quantum software.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Central claim rests on an unquantified and unvalidated comparison: 'high correspondence' has no matching metric, the manual baseline has no inter-rater reliability, and the time-saving claim is unmeasured.","rationale":"The reader's weakest assumption correctly identifies the manual taxonomy as an unvalidated ground truth. My stress-test concurs and extends the concern: beyond baseline reliability, the paper lacks any quantitative, reproducible comparison protocol. Section 4 says 'high correspondence' and 'tend to match or exceed' but never defines the row-matching procedure or reports agreement statistics. Section 3.2 does not state the number of expert raters or any inter-rater reliability check. The paper also labels LLM-identified scenarios as 'extra' without verifying they are true positives, which is essential because the claim of 'surpassing' the manual taxonomy rests on those extras being valid. Finally, the assertion of substantial time reduction is unsupported by any timing data, making the efficiency component of the central claim untestable. These concerns do not amount to an accusation of misconduct; the paper explicitly labels its results preliminary and future work, so a conditional accept is the fair verdict. Because my concern reinforces rather than overturns the reader's CONDITIONAL verdict, I recommend UNCHANGED, but with the additional conditions that artifacts be released and quantitative agreement and timing metrics be reported to settle the claim.","tokens_in":10396,"tokens_out":2919,"duration_ms":34919,"concrete_test":"Publish in the GitHub repository the full manual taxonomy (all rows and versions), the exact prompts, the raw outputs of all six tested models, and a scenario-matching script with an explicit matching rule (normalized strings or manually adjudicated row pairs). Then: (1) Have two independent QSE researchers each construct a taxonomy from the same 0.46.0 and 1.0.0 Qiskit release notes, and report pairwise inter-rater agreement (e.g., matched-row percentage and Cohen's kappa). (2) Compute precision, recall, and F1 of each LLM taxonomy against the manual taxonomy, and have independent experts judge the validity of each LLM 'extra scenario.' (3) Record wall-clock time for the manual taxonomy construction and for each LLM run. If the agreement metrics and precision/recall are not reported, the 'high correspondence' and 'suitable' claims remain unevaluable.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The paper's headline inference (Section 4, final paragraph) is that LLMs are 'plausible and suitable for automatic taxonomy generation of QSE migration scenarios, substantially reducing the required time.' This inference depends entirely on the reported comparison between the manual and LLM taxonomies. Three unsecured conditions underpin it. First, Section 3.2 describes the manual taxonomy as produced by the authors themselves, with no number of raters and no inter-rater agreement statistics, so there is no evidence that this baseline is a stable or complete ground truth. Second, Section 4 asserts 'high correspondence' without defining how scenarios were matched or reporting any quantitative metric (e.g., precision, recall, F1, or Cohen's kappa); without such measures, 'high correspondence' is an informal impression, not an empirical result. Third, the LLM-generated 'extra scenarios' are presented as an advantage, but they are never validated against any gold standard, so they may include hallucinations, duplicate rows, or overly fine-grained splits rather than genuinely missing migration scenarios. The 'substantially reducing the required time' clause is also asserted with no measurement of manual effort or LLM runtime anywhere in the manuscript. If the comparison protocol is informal and the baseline is unvalidated, the central claim cannot be independently checked, so the conditional verdict is appropriate but requires concrete evidence to confirm the claim.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper reports a feasibility study in which the authors construct a taxonomy of refactoring scenarios for migrating Qiskit code between versions, first manually from official documentation and then automatically by prompting several LLMs (gemma-3-27b-it, DeepSeek-R1-Distill-Qwen-32B, GPT-4o, GPT-4o-mini, DeepSeek-V3, DeepSeek-R1). The taxonomies are compared qualitatively in Section 4, and the authors conclude that LLMs are 'plausible and suitable' for automatic taxonomy generation and that they would 'substantially reduc[e] the required time.' The manuscript also provides a GitHub repository with scripts for documentation extraction, verification, and model access, and discusses future extensions such as RAG and full migration tooling.","tokens_in":10612,"tokens_out":5662,"duration_ms":59930,"significance":"If the central claim were backed by quantitative evidence, the contribution would be useful: a reproducible taxonomy of Qiskit migration scenarios could support benchmarking of automatic refactoring tools and guide QSE tooling. The paper has real strengths: it uses official Qiskit documentation as the source for both taxonomies, releases scripts to reproduce extraction and prompting, tests multiple open and commercial models, and candidly discusses hallucinations, context-length constraints, and the need for future validation. However, the evidence offered in the current version is primarily informal; the claimed correspondence is not measured, the manual baseline is not validated, and the promised unified taxonomy is not included. The main skeptical concern about the paper is justified: the headline inference depends on an unquantified comparison and an unmeasured time-saving claim, so it cannot currently be independently checked.","major_comments":[{"comment":"The paper's central inference—that LLM use is 'plausible and suitable' and would lead to 'substantially reducing the required time'—rests on qualitative statements such as 'high correspondence' and 'models behave the best.' No matching protocol, agreement metric, row counts, per-model breakdown, or repeated-run variance is reported. Moreover, because the prompt enforces the same column structure and a few-shot example as the manual taxonomy, structural alignment is partly by construction; the substantive question is scenario-level agreement, which is not measured. Please add a quantitative comparison (e.g., precision/recall/F1 or Cohen's kappa) with an explicit scenario-matching rule, and report results for each model and run.","section":"§4, final paragraph"},{"comment":"The manual taxonomy is used as the reference against which LLM output is judged, but the manuscript gives no information about who built it, how many people were involved, whether there was any inter-rater agreement, or how scenarios were selected and validated. It also says versions 0.46.0 and 1.0.0 were considered 'exhaustively' while later stating that 13 version updates were analyzed, which is internally inconsistent. A weak or unvalidated baseline makes 'extra scenarios' from the LLM uninterpretable, since they may reflect hallucinations or duplicates rather than genuine omissions. Please document the construction and validation of the baseline and clarify which versions are actually covered.","section":"§3.2"},{"comment":"The abstract and introduction promise a 'unified taxonomy' produced by integrating the manual and LLM taxonomies, but the full text never presents this artifact: there is no table, figure, or appendix showing the unified taxonomy, nor a list of the migration scenarios for the analyzed versions. Since this taxonomy is the paper's main contribution and the foundation for future work, the manuscript is incomplete without it. Please include the unified taxonomy (or clearly mark it as supplementary material with an accessible link).","section":"Abstract and §3.1"},{"comment":"LLM generation is stochastic, yet the paper does not report the number of independent runs, the temperature/sampling settings, or the aggregation/selection rule used for the reported outputs. A single run per model-prompt pair could easily produce favorable or unfavorable outcomes by chance, and the reported phases ('initial,' 'intermediate') appear to be based on informal observation rather than logged results. Please specify the sampling configuration and report variance across repeated runs.","section":"§3.3 and §4"},{"comment":"The claim that LLM-assisted taxonomy generation 'can save time of the SE expert' is plausible but unsupported: no manual effort (person-hours), LLM runtime, cost, or comparative workflow time is measured anywhere in the manuscript. Since the abstract and conclusion repeat the time-saving claim, it should either be measured (even roughly) or explicitly reframed as a conjecture.","section":"§5"}],"minor_comments":[{"comment":"There are several typographical errors that should be corrected: 'Leatest' for 'Latest,' 'chanel' for 'channel,' 'Qween' for 'Qwen,' and 'asses' for 'assess' in Sections 5 and 6.","section":"§3.2 and §3.3"},{"comment":"The GitHub URL is rendered with spaces ('qiskit llm experiment') and should be a proper clickable link.","section":"§3.3"},{"comment":"Figure 2 is described as examples of scenarios but is used to support a general claim about correspondence; the caption should clarify that it is illustrative and not a quantitative sample.","section":"§4, Figure 2"},{"comment":"Figure 4 is referenced in Section 4 ('See Fig. 4') before it is introduced; consider moving or renumbering figures so that references follow the figures.","section":"§4 and §5, Figure 4"},{"comment":"The phrase 'hybrid methodology' is used without a precise definition; it seems to mean combining manual analysis with LLM assistance, but this could be stated explicitly.","section":"§1"}],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"What you should know: this is a small, honest study that does something new. It applies LLMs to produce a taxonomy of Qiskit version-migration scenarios and compares it against a manual taxonomy built by the authors. The contribution is modest—a classification rather than a new principle—but it is a legitimate first application in QSE tooling, and the paper does not oversell itself.\n\nThe best part is the methodological scaffolding. The authors describe a pipeline with scripts for documentation extraction, verification, and model access, and they point to a public GitHub repository. They feed the LLMs the same official Qiskit documentation used for the manual taxonomy, and they restrict the comparison to the 0.46.0 to 1.0.0 major bump, which is a reasonable scoping. Prompt iteration is reported honestly, including initial failures. The paper's own text calls the results preliminary and lists concrete next steps.\n\nThe soft spots are real and they are in the paper, not the stress-test. Section 4's central claim—\"high correspondence\" and that LLMs are \"plausible and suitable\"—rests on informal inspection. There are no metrics: no counts, no F1, no kappa, no precision/recall. The manual taxonomy is the baseline, but it is described as produced by \"expert developers\" without stating how many, how scenarios were validated, or any inter-rater agreement. The LLM \"extra scenarios\" are presented as an advantage but never checked against a gold standard, so they could be hallucinations. The \"substantially reducing the required time\" assertion is never measured. And the taxonomies themselves are not in the manuscript, so the correspondence cannot be independently verified. All of this is fixable: ship the taxonomies and raw outputs, add quantitative agreement stats, and validate the manual baseline with independent raters.\n\nThe citation pattern is fine—related work on Q# refactoring and LLM code migration is cited, though the Qiskit-specific angle is genuinely new. No circularity beyond the unavoidable fact that the LLM was told to use the same columns and examples as the manual taxonomy, which the paper acknowledges indirectly.\n\nWho is this for? Researchers building AI-assisted quantum software migration tools, especially around Qiskit. It deserves a serious referee: the topic is timely, the reproducibility effort is real, and the flaws are addressable rather than fatal. I would send it to peer review, but the referee should insist on the artifacts and quantitative comparison before acceptance. For a workshop or short paper, minor additions would suffice; for a full journal, the validation needs to be substantially stronger.","headline":"A modest but honest first cut at using LLMs to build a Qiskit migration taxonomy, with a real reproducibility pipeline, yet the central 'high correspondence' claim is currently unsupported by any measurement.","tokens_in":11183,"tokens_out":1798,"would_cite":false,"duration_ms":20538,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"LLMs can generate a Qiskit migration taxonomy that largely matches—and sometimes exceeds—one built by expert developers, in far less time.","keywords":["Quantum Software Engineering","Large Language Models","taxonomy","Qiskit","code migration","refactoring","release notes"],"falsifier":"Have several independent expert teams each build a taxonomy of the same Qiskit version migrations, measure their inter-rater agreement, then run the LLM prompts against the validated consensus taxonomy; if overlap drops below a pre-agreed threshold (for example, recall under 80%) or if most LLM-only scenarios are rejected as hallucinations, the claim of suitability fails.","tokens_in":10178,"feed_emoji":"🤖","tokens_out":5962,"duration_ms":52611,"temperature":0.7,"pith_summary":"This paper claims that large language models can produce a taxonomy of code-migration scenarios for the Qiskit quantum programming library that is largely aligned with—and in places richer than—a taxonomy built manually by expert developers. To test this, the authors built two taxonomies of breaking changes between Qiskit versions (focusing on the jump from 0.46.0 to 1.0.0): one by hand from official documentation and release notes, and one by prompting several LLMs with the same documentation. The outputs were compared and merged into a unified taxonomy that categorizes scenarios by change type, affected Qiskit module, difficulty, and whether the impact is classical software engineering or quantum-specific. If the claim holds, LLM-assisted taxonomy generation could cut the expert time needed to catalog migration needs in fast-moving quantum libraries and supply a structured benchmark for evaluating automated migration tools.","feed_headline":"LLMs can draft Qiskit migration taxonomies that match expert ones","feed_subtitle":"Coupled with official docs, the models find scenarios experts miss and do it in a fraction of the time.","key_machinery":"The mechanism is a paired taxonomy-generation pipeline with two arms. The manual arm produces a taxonomy from Qiskit release notes and changelogs, with each scenario described by category, migration flow, summary, artifacts, source and target code examples, difficulty, and SE/QSE impact. The automatic arm feeds the same documentation into a system prompt with few-shot examples, instructing an LLM to emit rows in the same markdown schema. Comparative analysis of the two taxonomies—overlap, gaps in either direction, and richness of examples—is what grounds the inference. The classification dimensions themselves are the objects that make the comparison meaningful.","core_discovery":"The paper's central finding is that when LLM prompts include the official Qiskit documentation, the automatically generated taxonomy shows high correspondence with the manual expert taxonomy, and in several cases the LLM output surpasses it in descriptive precision, completeness of code examples, and detection of scenarios the experts did not list. From this the authors infer that using LLMs for automatic taxonomy generation of quantum software engineering migration scenarios is plausible and suitable, substantially reducing the time required. The effect is clearest for major version updates, where documentation is extensive and the API changes are disruptive.","pith_inferences":["The paper's evidence for agreement is qualitative; a quantitative score (recall/precision or inter-rater agreement against multiple experts) would convert 'high correspondence' into a measurable claim.","LLM-only scenarios flagged as 'extra' could be either genuine gaps in the manual taxonomy or hallucinations; the paper does not distinguish, so a follow-up study should have experts rate them.","Because Qiskit is Python-based and LLMs are known to be strongest at Python, the result may not transfer to quantum languages with C-like syntax such as Q#, which the paper itself acknowledges needs validation.","The manual taxonomy is treated as ground truth, yet its own reliability is unreported; a repeatability study with several independent expert taxonomies would make the comparison sound."],"forward_implications":["LLM-generated taxonomies can serve as a fast first pass, letting experts spend their time validating and refining rather than building the catalog from scratch.","The unified taxonomy becomes a reusable evaluation harness: migration tools can be scored per scenario, difficulty level, and QSE specificity.","Feeding official documentation directly into the prompt is the main quality lever, and context-length limits can be handled with retrieval augmentation or summarization.","Prioritizing tool development can now target the high-difficulty and quantum-specific scenarios that no classical refactoring tool covers."],"supporting_citations":[{"why":"Supplies the closest prior result—LLMs for automatic library migration in classical software—which this paper extends to the quantum domain.","marker":"[Almeida et al., 2024]"},{"why":"Provides empirical evidence of LLM code-refactoring capability, the baseline that makes expecting LLMs to handle quantum refactoring plausible.","marker":"[Cordeiro et al., 2024]"},{"why":"Proposes a catalog of quantum program refactoring in Q#, the conceptual precursor to a taxonomy for Qiskit version migration.","marker":"[Zhao, 2023]"},{"why":"Shows LLMs can be trained on Qiskit code, supporting the plausibility of LLMs operating within the Qiskit ecosystem.","marker":"[Dupuis et al., 2024]"},{"why":"Demonstrates LLM-based quantum code generation over PennyLane with retrieval augmentation, a related approach this work builds on.","marker":"[Asif et al., 2025]"},{"why":"Supplies the authoritative description of Qiskit and its architecture, grounding the migration scenarios analyzed in the taxonomy.","marker":"[Javadi-Abhari et al., 2024]"},{"why":"The retrieval-augmented generation approach the authors name as a way to handle documentation that exceeds model context length.","marker":"[Lewis et al., 2021]"}],"fun_headline_variants":["LLMs with docs match expert Qiskit migration taxonomies","AI taxonomy rivals expert Qiskit refactoring scenarios","LLM taxonomies surpass experts on Qiskit migration cases","Docs boost LLM taxonomy to expert level for Qiskit"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The comparison assumes the manually built taxonomy is a correct and complete baseline, but the paper does not report how many experts built it, whether they agreed with each other, or how scenarios were validated.","fun_headline_variants_meta":{"raw":{"variants":["LLMs with docs match expert Qiskit migration taxonomies","AI taxonomy rivals expert Qiskit refactoring scenarios","LLM taxonomies surpass experts on Qiskit migration cases","Docs boost LLM taxonomy to expert level for Qiskit"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000195,"raw_usage":{"total_tokens":1349,"prompt_tokens":926,"completion_tokens":423,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":542,"completion_tokens_details":{"reasoning_tokens":363}},"tokens_in":542,"tokens_out":423,"duration_ms":4462,"temperature":1.0,"reasoning_tokens":363,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-07T05:40:41.726978+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Have several independent expert teams each build a taxonomy of the same Qiskit version migrations, measure their inter-rater agreement, then run the LLM prompts against the validated consensus taxonomy; if overlap drops below a pre-agreed threshold (for example, recall under 80%) or if most LLM-only scenarios are rejected as hallucinations, the claim of suitability fails.","supporting_citations":[{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"The retrieval-augmented generation approach the authors name as a way to handle documentation that exceeds model context length."}],"review_version":1}