{
  "added_at": "2026-10-20",
  "additional_evidence_urls": [
    "https://github.com/Abdoulrazack1/Logic-Lens/blob/bb15aab345de64ef044a074d5e564fb087999032/package.json",
    "https://github.com/Abdoulrazack1/Logic-Lens/blob/bb15aab345de64ef044a074d5e564fb087999032/src/predictor.js",
    "https://github.com/Abdoulrazack1/Logic-Lens/blob/bb15aab345de64ef044a074d5e564fb087999032/src/model.js",
    "https://github.com/Abdoulrazack1/Logic-Lens/blob/bb15aab345de64ef044a074d5e564fb087999032/src/tf-setup.js",
    "https://github.com/Abdoulrazack1/Logic-Lens/blob/bb15aab345de64ef044a074d5e564fb087999032/src/semantic/semantic-analyzer.js",
    "https://github.com/Abdoulrazack1/Logic-Lens/blob/bb15aab345de64ef044a074d5e564fb087999032/tests/semantic.test.js",
    "https://github.com/Abdoulrazack1/Logic-Lens/blob/bb15aab345de64ef044a074d5e564fb087999032/CONTRIBUTING.md"
  ],
  "category": "javascript-electron",
  "description_en": "Experimental JavaScript source-analysis toolkit that classifies AST-token sequences into configured formula families after local training, and produces heuristic structure, call-graph and intent reports. It supports code-comprehension experiments; its predictions do not establish arbitrary program invariants or semantic equivalence.",
  "description_zh": "实验性 JavaScript 源码分析工具：本地训练后将 AST token 序列归入预设公式类别，并生成启发式结构、调用图和意图报告。它可用于代码理解实验，但预测结果不证明任意程序的不变量或语义等价。",
  "entry_url": "https://awesomerea.org/resources/logic-lens/",
  "entry_url_zh": "https://awesomerea.org/zh/resources/logic-lens/",
  "evidence_url": "https://github.com/Abdoulrazack1/Logic-Lens/blob/bb15aab345de64ef044a074d5e564fb087999032/README.md",
  "group": "tools",
  "id": "logic-lens",
  "json_url": "https://awesomerea.org/resources/logic-lens/record.json",
  "kind": "tool",
  "markdown_url": "https://awesomerea.org/resources/logic-lens/index.md",
  "markdown_url_zh": "https://awesomerea.org/zh/resources/logic-lens/index.md",
  "name": "Logic-Lens",
  "name_zh": "Logic-Lens：实验性 JavaScript 源码理解工具",
  "record_type": "resource",
  "relationship": "foundation",
  "requirements": [
    "The README documents Node.js 18 or newer. Package version 1.1.0 declares TensorFlow.js, its native Node binding and Acorn; the setup first tries the native TensorFlow binding and then falls back to the pure JavaScript CPU backend.",
    "Formula prediction requires local synthetic-data generation and training. The predictor expects model weights and metadata that are absent from the reviewed repository tree.",
    "Predictions select from a configured formula catalogue. The README’s accuracy/confidence examples are author-reported; heuristic intent and quality reports are not behavioral-equivalence proofs.",
    "Standalone semantic-test definitions cover structure, heuristic patterns and report shape. CONTRIBUTING mentions npm test, but package.json has no test script.",
    "README and package metadata declare MIT, but the reviewed commit has no LICENSE file."
  ],
  "requirements_zh": [
    "README 要求 Node.js 18 或更新版本。package 版本 1.1.0 声明依赖 TensorFlow.js、其原生 Node 绑定与 Acorn；初始化先尝试原生 TensorFlow 绑定，再回退到纯 JavaScript CPU 后端。",
    "公式预测需要在本地生成合成数据并训练。预测器所需的模型权重与元数据未包含在所审阅的仓库树中。",
    "预测从预设公式目录中选择类别。README 的准确率和置信度示例来自作者报告；启发式意图与质量报告也不是行为等价证明。",
    "独立的语义测试定义覆盖结构、启发式模式和报告格式。CONTRIBUTING 提到 npm test，但 package.json 没有 test 脚本。",
    "README 与 package 元数据声明 MIT，但所审阅提交没有 LICENSE 文件。"
  ],
  "targets": [
    "app"
  ],
  "url": "https://github.com/Abdoulrazack1/Logic-Lens",
  "verification": "source-reviewed",
  "verified_at": "2026-10-20"
}