{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "An agent looks good in demos, but you cannot tell which product tasks it fails.",
    "change": "Use the study's evaluation idea to inspect a small set of tasks with human-checked answers.",
    "baseline": "Your current evaluator and a manually reviewed reference set.",
    "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": "Agreement with human review, missed failures, false alarms, and evaluation cost per task.",
    "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": "2610.21359",
  "title": "SWE-at-Scale: Scalable Issue Synthesis from Historical Bug Fixes",
  "summary": "The method replaces manual curation of software-engineering benchmarks and heuristic LLM prompting by inverting real historical bug fixes into bug-induction supervision, producing executable tasks in new code contexts. The cost is a pipeline requiring a corpus of real fixes, repeated large-model sampling, and sandboxed execution to verify each synthesized task, none of which is free on a limited cloud budget. What can fail is unverified or non-reproducible tasks entering the benchmark, and diversity gains at high generation counts may mask diminishing novelty relative to actual production failure modes.",
  "rationale": "The framework targets benchmark and training-data generation for coding agents, which demands fix corpora, repeated model sampling, and execution infrastructure that exceed the reader's constrained setup without a quantified efficiency gain to justify adoption.",
  "technique": "Reverse-engineering historical fixes to synthesize SWE tasks",
  "family": "avaliacao_de_agente",
  "recommendation": "observar",
  "published": "2026-10-15",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.21359",
  "independent_implementations": 2,
  "total_implementations": 2,
  "stars": 4,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method replaces manual curation of software-engineering benchmarks and heuristic LLM prompting by inverting real historical bug fixes into bug-induction supervision, producing executable tasks in new code contexts. The cost is a pipeline requiring a corpus of real fixes, repeated large-model sampling, and sandboxed execution to verify each synthesized task, none of which is free on a limited cloud budget. What can fail is unverified or non-reproducible tasks entering the benchmark, and diversity gains at high generation counts may mask diminishing novelty relative to actual production failure modes.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.21359",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.21359",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
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      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
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    },
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      "dimension": "compute",
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    },
    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "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": ""
}