{"id":"831e7746-639b-4c3d-9a59-0456da49d1b4","arxiv_id":"2505.04040","paper_version":1,"verdict":"UNVERDICTED","confidence":"HIGH","novelty_score":4.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"The paper outlines planned research to use LLMs to detect, remove, and study redundant code in open-source AI projects.","lead":"This paper is a doctoral research proposal, not a completed study. It plans to use large language models to find and remove unnecessary code in AI software projects, and to interview developers about why redundant code appears.","discovery_kind":"unclear","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Planned validation equates passing existing tests with behavior preservation; this is the load-bearing assumption, and the paper itself flags it as a limitation in Section V.","rationale":"The reader's weakest assumption is exactly the load-bearing concern: LLM-generated optimizations are validated only by passing existing tests, and the paper itself concedes test coverage may be incomplete. My stress-test adds a concrete amplification: the protocol submits the entire file to the LLM and reintegrates it as a whole, so the optimization surface is large and the probability of touching untested behavior is correspondingly higher. The proposed quality metrics do not measure semantic equivalence, so they cannot compensate. This does not change the verdict. The paper is a doctoral research proposal with no experimental results, so there is nothing to verify or score; the correct disposition remains UNVERDICTED. The concern is a risk in the planned method, not an internal inconsistency in the proposal, and the limitations section already acknowledges the core issue. No ad hominem is intended; the critique is directed at the validation design, and the proposed differential-testing check is a concrete, inexpensive way to test whether the concern lands before committing to the full study.","tokens_in":5311,"tokens_out":2464,"duration_ms":25356,"concrete_test":"Run a pilot on a sample of AI-project files that the LLM optimizes and that pass the project's existing test suite. Apply differential testing or property-based testing: feed randomized inputs to both original and optimized versions and compare outputs, side effects, and raised exceptions. If any test-passing optimized file exhibits a behavioral mismatch, the proposed test-suite criterion is insufficient and the framework must require a stronger equivalence check before classifying an optimization as successful.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim is that the proposed LLM agent will optimize redundant code while \"preserving original functionality.\" For that claim to hold, the proposed validation must actually detect behavioral regressions. Section III states that optimized code is reintegrated and test cases are run to ensure all functionalities remain intact, and only code that passes is treated as successful. This makes test-suite passage the operational definition of behavior preservation. Section V concedes, however, that \"incomplete test case coverage may fail to detect functionality issues in optimized code.\" The risk is amplified by the design of submitting an entire file to the LLM: a large rewrite can alter many code paths at once, and AI codebases often contain data-dependent or non-deterministic behavior that unit tests do not exercise. The additional metrics (LOC, cyclomatic complexity, code churn) measure textual or structural properties, not semantic equivalence, so they do not rescue the success criterion. Since the proposal documents only whether tests pass or fail, silently changed behavior in test-passing output would be counted as a success, directly undermining the claim that functionality is preserved.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"This manuscript, identified as a doctoral research proposal, aims to address the perceived lack of studies on redundant code in AI system codebases by proposing the use of large language models (LLMs) to automatically detect, optimize, and reason about redundant code. It defines four research questions covering the prevalence and impact of redundancy in AI projects, the recurring patterns and causes, developers' perspectives, and the effectiveness of LLMs. The planned contributions include a catalog of redundancy reasons, an LLM-powered tool for optimization, and an evaluation framework based on test suite passage, static metrics (LOC, cyclomatic complexity, code churn), developer interviews, and user studies. The paper explicitly states that the project is in its early stages and that the evaluation framework may be refined, and it concludes with a limitations section acknowledging issues such as limited generalizability, test coverage, and subjective validation.","tokens_in":5466,"tokens_out":4759,"duration_ms":46996,"significance":"If executed as planned, the proposed work could produce a useful empirical characterization of redundancy in AI codebases and a practical tool for automated refactoring. The manuscript is clearly structured and honest in stating its limitations, which is a strength. However, the paper currently contains no implemented tool, no experimental results, and no derivations. Its central claim that LLMs can optimize redundant code while preserving functionality is a plan rather than a demonstrated result. The proposed validation procedure, which equates passing existing test suites with behavior preservation, is explicitly conceded by the authors to be potentially inadequate, undermining the key success criterion. As a journal submission, the manuscript does not yet offer a complete, evaluable scientific contribution, though it could serve as a basis for a research proposal or vision paper.","major_comments":[{"comment":"The central claim of preserving original functionality is operationally defined in Sections III and IV as passing existing test cases, yet Section V explicitly states that 'incomplete test case coverage may fail to detect functionality issues in optimized code.' This makes the validation circular: the LLM output is considered successful if the tests pass, even when the tests may not exercise all behaviors. Since RQ4 and Expected Outcome 2 depend on this criterion, the proposal's success metric is self-admittedly unreliable. The authors should propose a stronger equivalence check, such as differential testing, property-based testing, or manual review of a representative sample, and report the coverage of the test suites used.","section":"III/IV/V"},{"comment":"The plan to submit 'an entire file' to the LLM for optimization and then reintegrate the optimized file into the codebase is risky because a large rewrite can alter multiple code paths simultaneously, making it difficult to attribute any behavioral change to a specific optimization. The proposed tool later optimizes 'files one at a time,' but still processes whole files. The paper does not describe how individual changes will be isolated or validated. A stepwise, diff-based approach with verification at each transformation would be needed to support the claim of large-scale safe optimization.","section":"Section III"},{"comment":"The evaluation metrics listed (LOC, Cyclomatic Complexity, Code Churn) do not directly measure 'redundancy' as defined in the paper. These metrics capture size, structural complexity, and change volume, but they are not sufficient to detect duplicate logic, copy-paste code, or dead branches. Without an operational definition of redundancy and a corresponding measurement strategy, the stated outcomes in RQ1 and RQ2 are not refutable. The paper should specify how redundancy will be quantified and how the catalog of patterns will be empirically grounded.","section":"Section III"},{"comment":"The manuscript is a research proposal with no empirical results, no prototype, and no validation data. The abstract claims that 'this research addresses these gaps,' but the paper only describes a plan. For a journal publication in software engineering, the absence of any implemented system or evaluation means the central claims are unsupported. The authors could reposition this as a position paper, or better, complete and report a pilot study to demonstrate the feasibility of the proposed approach.","section":"Entire manuscript"}],"minor_comments":[{"comment":"The text contains numerous broken words and spaces, such as 'a nd', 'introdu cing', and 're dundant', likely resulting from PDF extraction. The authors should ensure that the final manuscript is free of such artifacts.","section":"Abstract and full text"},{"comment":"The sentence 'dead code refers to programming that is never executed' uses 'programming' where 'program code' or 'source code' would be more precise.","section":"Section I"},{"comment":"The phrase 'Cyclomatic Complexity (logical complexity)' is imprecise; cyclomatic complexity specifically measures the number of linearly independent paths through a program's control flow, not general logical complexity.","section":"Section III"},{"comment":"The citation to Li et al. [17] for selecting open-source projects does not describe the methodology. Briefly summarizing what that methodology is would help the reader assess the validity of the project selection.","section":"Section III"},{"comment":"There is a typographical issue with the phrase 'e.g., Code Maintainability Index and Cyclomatic Complexity may not fully capture' - the comma after 'e.g.' is missing, and the sentence could be split for readability.","section":"Section V"}],"recommendation":"reject","confidential_remarks":"This is a research proposal, not a completed study. It may be suitable for a doctoral symposium or a vision track in a workshop, but it does not meet the bar for a full journal publication. The authors should either conduct a substantial study or recast the paper as a position piece. The identified circularity in the validation is a fundamental issue that would require new experimental work to resolve."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Colleague,\n\nQuick read on arXiv:2505.04040. It's a doctoral research proposal, not a research paper. There are no results, no data, no code, no derivations. That's not inherently bad—proposals are a legitimate genre—but it means the usual question 'is the result correct?' doesn't apply. The paper is best read as a statement of planned work.\n\nWhat it does well: the focus on AI codebases is a reasonable gap, since most redundancy work targets general web or Java projects. The research questions are concrete and separable. The limitations section is unusually honest: it explicitly concedes that incomplete test coverage may miss functionality issues, that LLM training-data bias is a threat, and that developer feedback is subjective. That level of candor is welcome.\n\nThe soft spot is exactly one it names itself. The planned validation equates passing the existing test suite with 'preserving original functionality' (Section III). If the test suite has gaps—and the paper admits it might—then an LLM rewrite that passes tests while silently changing behavior would be counted as a success. The design of submitting an entire file to the LLM amplifies this, because a large rewrite can touch many code paths at once. The other metrics (LOC, cyclomatic complexity, code churn) are textual/structural and don't speak to semantic equivalence. So the operational success criterion is weaker than the stated goal. This is a design flaw in the plan, not a flaw in reported results, because there are no reported results. Still, it's the load-bearing assumption, and the paper itself flags it. Good for them for admitting it; they need to address it with differential testing, mutation testing, or at least a manual review sample before the claim 'preserves functionality' is usable.\n\nAnother minor point: submitting the entire file to GPT-4, Gemini, and Llama is expensive and noisy. There is no discussion of output parsing, token limits, or how many files are realistically processed. That's a practical concern for the feasibility of the plan.\n\nWho is this for? Someone supervising or evaluating early-stage doctoral work in LLM-based software maintenance would find it a useful statement of intent. A reader looking for empirical findings will be disappointed.\n\nRecommendation: I would not send this to a full peer-review track as a research paper. For a doctoral symposium or a position-paper track, it's fine. If it arrives at a regular SE venue, desk reject, with a pointer to the limitation they should fix. The author is thinking clearly; the proposal is honest and well-scoped. Just nothing to peer-review yet.","headline":"A well-written doctoral research proposal with no experimental content; the honest limitations section names the real validity threat, so there is nothing to verify yet.","tokens_in":5940,"tokens_out":1991,"would_cite":false,"duration_ms":18796,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":false},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"This paper proposes using large language models to automatically detect and remove redundant code in AI projects, verifying each rewrite against the project's own test suite.","keywords":["code redundancy","LLM","code optimization","AI codebases","technical debt","refactoring","dead code"],"falsifier":"Collect a set of AI codebases that have both their original test suites and a separate, more exhaustive held-out suite. If the LLM agent's optimized code passes the original tests but fails the held-out suite on inputs the original tests did not cover, the central claim that passing tests proves preserved functionality fails.","tokens_in":5114,"feed_emoji":"🧹","tokens_out":5211,"duration_ms":48835,"temperature":0.7,"pith_summary":"This paper is a research proposal, not a report of finished results. It argues that redundant code in AI projects is understudied and that large language models can close the gap by automatically detecting redundancies, identifying their recurring patterns and root causes, and optimizing code while preserving behavior. A sympathetic reader would care because manual redundancy removal is risky and laborious, and the paper promises an automated agent that verifies each rewrite with the project's own tests. The planned contributions are a prevalence analysis, a catalog of causes and patterns, and a prototype agent.","feed_headline":"LLMs could auto-remove redundant code in AI codebases","feed_subtitle":"A doctoral proposal plans an LLM agent that rewrites files, reruns tests, and maps why code duplication creeps in.","key_machinery":"The central mechanism is the LLM-powered refactoring agent, which processes files one at a time, replaces them with LLM-suggested clean versions, and accepts or rejects each rewrite based on whether the project's test suite still passes. Supporting it are static quality metrics—lines of code, cyclomatic complexity, code churn, maintainability index, bug density—that quantify whether an optimization actually reduced complexity.","core_discovery":"The paper's central claim is that redundant code in AI system codebases has identifiable recurring patterns, and that large language models can reliably detect and remove such code at scale without changing program behavior. It stakes this on a proposed pipeline: feed entire source files to one of three LLMs, reintegrate the optimized file, run the project's tests, and keep only rewrites that pass. The paper also maintains that the reasons developers introduce redundancy—copy-paste effects, repetitive logic, overused conditionals, outdated practices—can be catalogued and validated with developers, and that these patterns plus quality metrics can drive an automated refactoring agent.","pith_inferences":["Editorial inference: the author leaves implicit that file-level whole-file rewrites could be paired with static-analysis hints to flag candidate redundancies first, reducing LLM cost and improving precision; that combination is a natural extension but is not proposed in the paper.","Editorial inference: the approach would likely generalize beyond AI projects to any Python or JavaScript codebase, since the redundancy patterns cited are language-level, but the paper deliberately limits its claims to open-source AI projects.","Editorial inference: the pass-tests criterion is only as strong as the suite itself; on projects with sparse coverage, the agent could silently change behavior, a risk the paper acknowledges in its limitations section."],"forward_implications":["If LLMs can strip redundancy at scale, developers of AI systems would spend less time untangling duplicated logic and more time on features and bug fixes.","The catalog of redundancy patterns and causes could guide coding standards and linters to prevent duplication at creation rather than cleaning it up later.","The test-suite verification loop suggests an automated refactoring tool that is safest on projects with strong test coverage, since it relies on those tests to catch behavior changes.","Quality metrics such as maintainability index and cyclomatic complexity could provide before-and-after evidence that redundancy removal measurably improves code quality.","If the agent works, it would reduce technical debt in AI codebases, where large data-processing and computation-heavy code makes redundancy especially costly."],"supporting_citations":[{"why":"Reports large-scale dead-code removal at Meta, establishing that unused code accumulates in real codebases and that automated deletion is valuable.","marker":"[6]"},{"why":"Multi-study investigation showing dead code harms both maintenance and design, which motivates the paper's problem statement.","marker":"[7]"},{"why":"Exploratory study finding hundreds of redundant method pairs across repositories, evidence that redundancy is widespread beyond dead code.","marker":"[10]"},{"why":"Supplies the methodology for selecting open-source software projects to analyze.","marker":"[17]"},{"why":"Provides one of the three large language models the proposed pipeline relies on.","marker":"[18]"},{"why":"Provides a second of the three large language models used for detection and optimization.","marker":"[19]"},{"why":"Provides the third large language model used for detection and optimization.","marker":"[20]"},{"why":"Defines cyclomatic complexity, one of the metrics used to measure whether optimization reduces logical complexity.","marker":"[22]"},{"why":"Defines the maintainability index, a metric the paper uses to assess the impact of redundancy and cleanup.","marker":"[24]"}],"fun_headline_variants":["LLM agent proposed to auto-detect and remove redundant code in AI systems","Doctoral proposal: LLM agent to purge redundant code from AI codebases","LLMs to spot and strip redundancy in AI code without breaking tests","Plan: LLM agent refactors AI codebase, deletes redundancy, passes tests"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The load-bearing premise is that an LLM rewrite that passes the project's existing test suite has preserved the original behavior; the paper concedes incomplete test coverage could mask regressions.","fun_headline_variants_meta":{"raw":{"variants":["LLM agent proposed to auto-detect and remove redundant code in AI systems","Doctoral proposal: LLM agent to purge redundant code from AI codebases","LLMs to spot and strip redundancy in AI code without breaking tests","Plan: LLM agent refactors AI codebase, deletes redundancy, passes tests"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000291,"raw_usage":{"total_tokens":1661,"prompt_tokens":867,"completion_tokens":794,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":483,"completion_tokens_details":{"reasoning_tokens":710}},"tokens_in":483,"tokens_out":794,"duration_ms":8521,"temperature":1.0,"reasoning_tokens":710,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-15T23:38:13.912137+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Collect a set of AI codebases that have both their original test suites and a separate, more exhaustive held-out suite. If the LLM agent's optimized code passes the original tests but fails the held-out suite on inputs the original tests did not cover, the central claim that passing tests proves preserved functionality fails.","supporting_citations":[{"cited_title":"Dead code removal at meta: Automatically deleting million s of lines of code and petabytes of deprecated data,","cited_arxiv_id":null,"evidence_quote":"Reports large-scale dead-code removal at Meta, establishing that unused code accumulates in real codebases and that automated deletion is valuable."},{"cited_title":"A multi- study investigation into dead code,","cited_arxiv_id":null,"evidence_quote":"Multi-study investigation showing dead code harms both maintenance and design, which motivates the paper's problem statement."},{"cited_title":"An exploratory study of functional redundancy in code repo sitories,","cited_arxiv_id":null,"evidence_quote":"Exploratory study finding hundreds of redundant method pairs across repositories, evidence that redundancy is widespread beyond dead code."},{"cited_title":"Exploring factors and metrics to select open source software components for integ ration: An empirical study,","cited_arxiv_id":null,"evidence_quote":"Supplies the methodology for selecting open-source software projects to analyze."},{"cited_title":"Cyclomatic complexity,","cited_arxiv_id":null,"evidence_quote":"Defines cyclomatic complexity, one of the metrics used to measure whether optimization reduces logical complexity."},{"cited_title":"Developmen t and application of an automated source code maintainability in dex,","cited_arxiv_id":null,"evidence_quote":"Defines the maintainability index, a metric the paper uses to assess the impact of redundancy and cleanup."}],"review_version":1}