{
  "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": "2609.28750",
  "title": "Soundness Checking of Taint Flow Models",
  "summary": "It replaces manual authoring of taint flow models for library methods, and partially replaces full inter-procedural taint analysis, with an LLM guess followed by a lightweight symbolic soundness check. It costs the implementation and maintenance of the checking pipeline (type system, pointer analysis, recursive callee-model verification) plus LLM API calls, which fits constrained infrastructure only if the underlying static-analysis tooling already exists for the target language. It can fail when the lightweight analyses cannot prove the deduced must-not-flows (the remaining 7% of methods), when the LLM produces imprecise models that inflate verification effort, and it currently has evidence only for Go codebases.",
  "rationale": "The approach is sound and low-cost per model but only matters if the team actually runs static taint analysis in production, so it warrants a workload-specific pilot rather than adoption or dismissal.",
  "technique": "LLM-generated taint models with symbolic soundness checking",
  "family": "agentes_de_codigo",
  "recommendation": "testar",
  "published": "2026-09-23",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.28750",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
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      "dimension": "compatibility",
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  ],
  "risks": [],
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
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  "equations_status": "not_fetched",
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