{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Your coding assistant makes changes that still need substantial repair before merging.",
    "change": "Try the technique on held-out issues from a repository with reliable tests.",
    "baseline": "The same coding agent, repository snapshot, tools, and token budget.",
    "steps": [
      "Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.",
      "Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.",
      "Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.",
      "Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test."
    ],
    "measure": "Correct patches, regression failures, human repair time, and total cost per accepted change.",
    "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation."
  },
  "builder_review": null,
  "arxiv_id": "2610.21925",
  "title": "Benchmarking and Enhancing Design-Aware Code Review in the Linux Kernel",
  "summary": "DesignLore replaces generic defect-focused automated review with retrieval of design decisions extracted from historical review discussions, organized into an adaptive taxonomy and injected into the review agent's context. The cost is building and maintaining a per-project knowledge base from review history plus retrieval and taxonomy-maintenance overhead; it runs on third-party API backbones, so no local training or cluster is required. Absolute performance remains low (F1 0.259), the method depends on a mature review history such as the Linux kernel's, and it can fail on projects with sparse or unstructured review discussions or where retrieved precedents are stale or inapplicable.",
  "rationale": "The approach is API-compatible and validated on realistic workloads, but the low absolute F1 and dependence on rich historical review data mean its value must be confirmed on the reader's own repositories before production adoption.",
  "technique": "Historical design-knowledge retrieval for code review",
  "family": "agentes_de_codigo",
  "recommendation": "testar",
  "published": "2026-10-15",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.21925",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "DesignLore replaces generic defect-focused automated review with retrieval of design decisions extracted from historical review discussions, organized into an adaptive taxonomy and injected into the review agent's context. The cost is building and maintaining a per-project knowledge base from review history plus retrieval and taxonomy-maintenance overhead; it runs on third-party API backbones, so no local training or cluster is required. Absolute performance remains low (F1 0.259), the method depends on a mature review history such as the Linux kernel's, and it can fail on projects with sparse or unstructured review discussions or where retrieved precedents are stale or inapplicable.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.21925",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "DesignLore achieves F1 0.259 on design trade-off detection versus 0.213 for the strongest baseline, a 21.6% relative improvement, with gains reported across agent systems and backbones.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.21925",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
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      "next_check": "",
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      "source_page": null,
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    },
    {
      "dimension": "compute",
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      "source_page": null,
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    },
    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "security",
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      "finding": "",
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      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "risks": [],
  "minimum_test": [],
  "open_questions": [],
  "independent_tests": [],
  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}