{"id":"f5d2f2b5-cc65-4009-b4d7-4e8affc55a8b","arxiv_id":"2506.14535","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":5.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"A structured taxonomy of Qiskit migration scenarios improves GPT-4's line-level refactoring precision from 0.32 to 0.55 and recall from 0.35 to 0.62 on 25 synthetic snippets.","lead":"Tests whether adding a structured list of Qiskit API changes to GPT-4's prompt helps it find outdated code and suggest fixes. In 25 synthetic programs, the taxonomy raised correct line-level fixes from 29 to 50 of 81 affected lines.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The paper's central evidence is a self-aligned benchmark: snippets are constructed from the same taxonomy used as the intervention, and scoring is manual, so the reported 50-vs-29 line improvement may reflect pattern matching to the taxonomy rather than generalizable migration assistance.","rationale":"I read the paper in good faith: the proposed methodology is coherent, the comparison with and without taxonomy is a reasonable controlled design, and the results are directionally positive. The reader's conditional verdict is appropriate. My stress-test focuses on the single most load-bearing condition for the central claim: that the measured benefit of the taxonomy reflects genuine migration assistance rather than an artifact of a self-aligned evaluation. The snippets are explicitly constructed to embed patterns from the same taxonomy that is later inserted into the prompt, which creates a closed loop from taxonomy to test cases to scoring. The manual scoring without inter-rater reliability, plus the numerical inconsistency in the scenario-level rows of Table II, strengthens the need for independent validation. The refactoring-level counts (50+31=81 with taxonomy; 29+52=81 without) are internally consistent, so I do not think the paper should be rejected; it should remain conditional pending the proposed real-code replication.","tokens_in":10247,"tokens_out":6818,"duration_ms":71876,"concrete_test":"Run a blind replication on real, public Qiskit migration commits (e.g., GitHub PRs that update code from Qiskit at or below 0.45 to 0.46, such as replacing qiskit.execute or other deprecated calls), using the same with/without-taxonomy prompts but scoring outputs by two independent annotators who do not know which condition produced each response; report Cohen's kappa and line-level precision/recall. If the with-taxonomy advantage shrinks to less than 10 percentage points on these real cases, or if annotator agreement is below kappa=0.6, the paper's central claim that the taxonomy enables effective migration is not supported by the current evidence.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The most load-bearing weakness is that the benchmark is constructed from the same taxonomy that is the intervention. Section IV says the 25 snippets were 'constructed to expose the model to known patterns from the taxonomy,' so the with-taxonomy prompt contains the very scenario examples used to author the test code. The model can therefore succeed by copying template refactorings from the taxonomy's example columns, and the Discussion indeed reports occasional 'reuse of placeholder variables copied verbatim from the taxonomy examples.' Manual scoring by two experts without inter-rater reliability further weakens the 50-vs-29 line-level result. In addition, Table II's scenario-level confusion matrix is internally inconsistent: TP=12 and FN=3 imply 15 total refactor-needed scenarios, not the 21 stated in Section V, and TN=1 with four negative snippets implies FP=3, not 9. This does not refute the direction of the effect, but it means the reported precision/recall gains are unvalidated upper bounds, not established estimates.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes a methodology for using large language models (LLMs) to refactor Qiskit code across versions, guided by a taxonomy of migration scenarios extracted from official Qiskit documentation and release notes. The authors evaluate their approach on 25 synthetic Python snippets, comparing the performance of gpt-4-0613 with and without the taxonomy included in the prompt. They report that the taxonomy improves scenario identification and line-level refactoring suggestions, with the key quantitative result being an increase in correct refactoring suggestions from 29/81 to 50/81 lines. The paper contributes the taxonomy, the prompts, and a publicly available repository.","tokens_in":10345,"tokens_out":4115,"duration_ms":45239,"significance":"If the result holds, the paper offers a practical and inexpensive way to assist Qiskit migration using LLMs, and the public release of prompts, taxonomy, and scripts supports reproducibility. The direction of the effect—access to structured migration knowledge improving LLM performance—is plausible and consistent with related work. However, the significance is currently tempered by three issues: the benchmark snippets are constructed from the same taxonomy that serves as the intervention, the manual scoring lacks inter-rater reliability, and Table II contains an internal inconsistency in the scenario-level confusion matrix. These issues are fixable but affect the strength of the central empirical claim.","major_comments":[{"comment":"The benchmark construction is coupled with the intervention. The paper states that the 25 snippets were 'constructed to expose the model to known patterns from the taxonomy,' and the taxonomy is exactly the context added in the treatment condition. This means the with-taxonomy model can succeed by retrieving or copying example refactorings from the taxonomy's example columns, which is supported by the Discussion's admission of 'reuse of placeholder variables copied verbatim from the taxonomy examples.' I would like to see either a holdout set of real-world Qiskit code or release-note-derived snippets that were not used when building the taxonomy, or an explicit evaluation of whether the model's correct answers depend on verbatim pattern matching.","section":"Section IV, first paragraph"},{"comment":"Table II contains a numerical inconsistency in the scenario-identification confusion matrix. The text states that 21 of 25 snippets required refactoring and 4 were negative. With TP=12 and TN=1, the remaining 3 of the 4 negative snippets imply FP=3, not the reported FP=9; conversely, with 21 positive snippets and TP=12, FN should be 9, not 3. The reported recall values also do not follow: 12/(12+3)=0.80, not 0.85, and 10/(10+3)=0.77, not 0.76. Please correct the matrix and the corresponding precision/recall values, and clarify whether 'scenario' means snippets, individual migration issues, or something else.","section":"Section V, Table II"},{"comment":"The line-level scoring is the basis for the headline 50-vs-29 improvement, but the paper reports that the two experts evaluated independently 'without formal inter-rater reliability measures.' The rubric categories (OK, OK-, X+, X) require judgment, and there is no reported agreement statistic or resolution procedure. Please provide inter-rater agreement (e.g., Cohen's kappa) on a subset, or otherwise justify that the scoring is sufficiently reliable to support the claimed precision/recall gains.","section":"Section IV, manual evaluation paragraph"},{"comment":"The abstract and introduction make claims about 'LLMs' in general, but the experiments use a single OpenAI model (gpt-4-0613). The Discussion correctly acknowledges this as a limitation, but the central claim would be strengthened substantially by testing at least one additional model, ideally a code-specialized one, even on a subset of the snippets. In addition, no confidence intervals or significance tests are reported for the 81-line and 21-scenario comparisons; given the small scenario-level counts, I would like to see a bootstrap or a matched-pair test to establish that the observed differences are not noise.","section":"Section VI, 'It is also worth mentioning...'"}],"minor_comments":[{"comment":"The last sentence contains a typo: 'to asses the capabilities' should be 'to assess the capabilities.'","section":"Abstract"},{"comment":"The ordinal suffixes in the author affiliations are incorrect: '2rd' and '3nd' should be '2nd' and '3rd.'","section":"Author block"},{"comment":"The caption contains a grammatical error: 'The were a total of 50 invocations' should read 'There were a total of 50 invocations.'","section":"Figure 1 caption"},{"comment":"The model name 'Qween' in the paragraph on d'Aloisio et al. appears to be a typo for 'Qwen' or a similarly named model; please correct it.","section":"Section II, Related Work"},{"comment":"The paper says the taxonomy was generated with 'ChatGPT 4.1,' while the evaluation uses 'gpt-4-0613'; please clarify whether the model that generated the taxonomy is the same as the model evaluated, since this affects the interpretation of the intervention.","section":"Section III"},{"comment":"The GitHub repository URL contains a space ('qiskit experiments tlisc'), which will break the link; please provide the correct URL.","section":"Reference [20]"}],"recommendation":"major_revision","confidential_remarks":"The manuscript appears to be a direct extension of the authors' prior taxonomy work [19], so the novelty overlap with that paper should be checked. The main risk for publication is the self-aligned benchmark: the taxonomy is both the intervention and the source of the test snippets. A real-world holdout, even of modest size, would make the central claim substantially more convincing. The Table II inconsistency is straightforward to fix but currently prevents the reader from trusting the scenario-level precision/recall numbers."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Quick take: this is a reasonable incremental study. The genuinely new thing is the empirical comparison: giving GPT-4 a structured taxonomy of Qiskit 0.46 migration scenarios does seem to improve both scenario identification and line-level refactoring suggestions over prompting without the taxonomy. The taxonomy itself is a useful artifact, and they ship prompts, data, and evaluation scripts. That counts for something.\n\nWhere it earns credit: the methodology is mostly transparent, the manual rubric is sensible, and the paper is honest about its own weaknesses. They explicitly note that the model sometimes copied placeholder variables from the taxonomy examples, and they acknowledge that only one model and one target version were tested.\n\nThe soft spots are real but not fatal. The biggest is benchmark circularity: the 25 synthetic snippets were constructed from the same taxonomy that is the intervention. So the with-taxonomy prompt contains the scenario examples used to author the test code. The model can partially succeed by matching templates rather than by genuine migration reasoning. That doesn't kill the claim, not least because the refactoring suggestions were independently checked against Qiskit API correctness, but it means the scenario-identification gain is not measured on neutral ground.\n\nThe scoring is also weaker than the numbers suggest. Two experts reviewed manually without formal inter-rater reliability, and Table II has an internal inconsistency: TP=12, FN=3 at scenario level implies 15 refactor-needed scenarios, but the text says 21. TN=1 with four negative snippets implies FP=3, not the reported 9. That makes the precision/recall values unreliable as published, though not the sign of the effect.\n\nThe central argument—taxonomy guidance helps—holds up directionally, but the magnitude is unvalidated. The paper deserves a serious referee, and with revisions it can be a decent tooling contribution for the quantum software engineering community. If I were editing, I'd send it out, with a request for the authors to fix the inconsistency, report inter-rater agreement, and ideally add a small set of real-world Qiskit code samples to break the circularity.","headline":"Useful incremental contribution with a self-aligned benchmark; the direction of the effect is plausible, but the headline numbers need corroboration before being trusted.","tokens_in":10921,"tokens_out":2002,"would_cite":false,"duration_ms":20165,"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":"Structured migration taxonomies from release notes materially improve an LLM's ability to identify and fix outdated Qiskit API usage.","keywords":["Qiskit","code migration","large language models","refactoring","taxonomy","quantum software engineering","API evolution","prompt engineering"],"falsifier":"Take a corpus of real open-source Qiskit projects with known migration commits from versions 0.41 to 0.45 up to 0.46, run the same taxonomy prompt on the pre-migration files, and score line-level suggestions against the actual committed changes: the claim predicts precision and recall near or above the reported 0.55 and 0.62, with no larger false-positive rate on files that needed no migration.","tokens_in":9988,"feed_emoji":"⚛️","tokens_out":5027,"duration_ms":49774,"temperature":0.7,"pith_summary":"The paper tries to establish that giving a large language model a compact, structured taxonomy of Qiskit migration scenarios, extracted from official release notes, materially improves its ability to spot outdated API usage and propose workable refactors. In the reported 25-snippet test set, where 21 snippets contained migration issues and 81 lines needed changes, adding the taxonomy raised correct refactoring suggestions from 29 to 50 lines, lifting precision from 0.32 to 0.55 and recall from 0.35 to 0.62. The authors argue that a taxonomy is condensed enough to fit the model's context window without retrieval or chunking, letting the model spend its attention on the actual code. If correct, this offers a low-cost path to partially automated migration for fast-evolving quantum software frameworks.","feed_headline":"Taxonomy guidance lifts correct Qiskit refactors from 29 to 50 lines","feed_subtitle":"Adding a structured migration taxonomy to the prompt raised refactoring precision from 0.32 to 0.55 in a 25-snippet test.","key_machinery":"The load-bearing object is the version-specific migration taxonomy, a markdown table of 43 scenarios built from Qiskit's release notes and documentation, with fields for category, migration flow, summary, artifacts, example source and target code, difficulty, impact, and references. The prompt asks the model to return a markdown table mapping line numbers to scenario IDs, artifacts, and refactoring suggestions, and to mark unmatched findings with an asterisk. The taxonomy does the work by compressing an entire version's breaking changes into a form that fits the model's context window; the structured output does the work by making every suggestion attributable to a named scenario.","core_discovery":"The paper's central claim is that exposing gpt-4-0613 to a compact, structured taxonomy of Qiskit migration scenarios, rather than relying only on its memorized knowledge, is what makes LLM-assisted migration work. On 25 synthetic snippets, 21 containing migration issues totaling 81 lines needing changes, the taxonomy raised correct refactoring suggestions from 29 to 50 lines, lifting precision from 0.32 to 0.55 and recall from 0.35 to 0.62, while cutting incorrect suggestions from 61 to 40 and missed lines from 52 to 31. Scenario identification also improved from 10 to 12 of 21 correctly identified refactor-needed scenarios. The authors interpret this as evidence that domain-specific migration knowledge, encoded as a taxonomy, materially improves both the identification and the resolution of version-drift problems in quantum code.","pith_inferences":["On a real-codebase corpus, the measured gain may shrink because real imports and usages do not map onto taxonomy examples as neatly as synthetic snippets crafted around the same taxonomy.","A strong test would generate a fresh taxonomy for Qiskit 2.0, a version released after the model's training data, and check whether the precision and recall gains persist when the model cannot rely on remembered documentation.","The same prompt-plus-taxonomy recipe could be applied to other Python SDKs that publish versioned release notes, treating release-note-derived taxonomies as a general migration aid.","The line-level scoring rubric could be automated, turning the manual expert review into a cheaper evaluation harness for comparing taxonomy quality and prompt variants."],"forward_implications":["For a new Qiskit release, a taxonomy can be produced from release notes and attached to the prompt, giving the model version-specific guidance without fine-tuning or retrieval.","The structured table output lets developers audit each suggested change against a scenario ID, making LLM refactoring suggestions reviewable rather than opaque.","Taxonomy guidance cuts both false positives (from 61 to 40 incorrect suggestions) and false negatives (from 52 to 31 missed lines) in the reported test set, shifting migration work from hunting for breakages to verifying flagged lines.","The negative test cases show the method still over-flags: 3 of 4 compatible snippets received unnecessary migration suggestions, so human review remains necessary."],"supporting_citations":[{"why":"Supplies the migration taxonomy itself, the structured knowledge injected into the prompt and the object whose effect the paper measures.","marker":"[19]"},{"why":"Shows that carefully designed GPT-4 prompts can perform Python library migration, providing the baseline prompting methodology this paper adapts to Qiskit.","marker":"[1]"},{"why":"Establishes the inter-framework quantum migration setting (PennyLane to Qiskit) that the paper contrasts with intra-framework version migration.","marker":"[2]"},{"why":"Represents fine-tuning an LLM on Qiskit code for generation, the alternative to prompt-level taxonomy guidance that motivates the paper's comparison.","marker":"[7]"},{"why":"Provides a large-scale industrial code-migration approach whose indexing strategy the paper positions its taxonomy-based method against.","marker":"[25]"},{"why":"Catalogs prompt-engineering techniques, grounding the paper's design choices and its discussion of chain-of-thought and rephrase-and-respond strategies.","marker":"[18]"}],"fun_headline_variants":["Taxonomy prompt lifts LLM Qiskit refactor precision from 0.32 to 0.55","Structured migration taxonomy boosts LLM refactor recall to 0.62","LLM refactors 21 more Qiskit lines when guided by taxonomy","Taxonomy-guided prompts cut missed Qiskit migration lines by 21"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The measured benefit of the taxonomy rests on 25 synthetic snippets, hand-written by the authors to embed scenarios from the same taxonomy placed in the prompt; if those snippets are not representative of real Qiskit code, the gain may not transfer.","fun_headline_variants_meta":{"raw":{"variants":["Taxonomy prompt lifts LLM Qiskit refactor precision from 0.32 to 0.55","Structured migration taxonomy boosts LLM refactor recall to 0.62","LLM refactors 21 more Qiskit lines when guided by taxonomy","Taxonomy-guided prompts cut missed Qiskit migration lines by 21"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000483,"raw_usage":{"total_tokens":2376,"prompt_tokens":929,"completion_tokens":1447,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":545,"completion_tokens_details":{"reasoning_tokens":1358}},"tokens_in":545,"tokens_out":1447,"duration_ms":10924,"temperature":1.0,"reasoning_tokens":1358,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-07T00:16:46.594751+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Take a corpus of real open-source Qiskit projects with known migration commits from versions 0.41 to 0.45 up to 0.46, run the same taxonomy prompt on the pre-migration files, and score line-level suggestions against the actual committed changes: the claim predicts precision and recall near or above the reported 0.55 and 0.62, with no larger false-positive rate on files that needed no migration.","supporting_citations":[{"cited_title":"Taxonomy of migration scenarios for qiskit refactoring using llms","cited_arxiv_id":null,"evidence_quote":"Supplies the migration taxonomy itself, the structured knowledge injected into the prompt and the object whose effect the paper measures."},{"cited_title":"Automatic Library Migration Using Large Language Models: First Results","cited_arxiv_id":"2408.16151","evidence_quote":"Shows that carefully designed GPT-4 prompts can perform Python library migration, providing the baseline prompting methodology this paper adapts to Qiskit."},{"cited_title":"Migrating Code At Scale With LLMs At Google, April","cited_arxiv_id":null,"evidence_quote":"Provides a large-scale industrial code-migration approach whose indexing strategy the paper positions its taxonomy-based method against."},{"cited_title":"A Systematic Survey of Prompt Engineer- ing in Large Language Models: Techniques and Applications, February","cited_arxiv_id":null,"evidence_quote":"Catalogs prompt-engineering techniques, grounding the paper's design choices and its discussion of chain-of-thought and rephrase-and-respond strategies."}],"review_version":1}