hangshuo652
133 lines
5.2 KiB
Python
133 lines
5.2 KiB
Python
"""
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HINA 程序分类器 — L1 关键字规则 + 确信度计算。
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通过 COBOL 源码中的关键字匹配进行程序分类，支持多级确信度判定。
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"""
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from __future__ import annotations
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from typing import Any
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# ── L1 规则 ──────────────────────────────────────────────────────────────
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# 格式: (分类名称, [关键字列表], 置信度阈值)
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L1_RULES: list[tuple[str, list[str], float]] = [
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("DB操作", ["EXEC SQL"], 0.95),
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("子程序调用", ["CALL", "LINKAGE SECTION"], 0.90),
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("IS INITIAL", ["IS INITIAL"], 0.99),
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("SYSIN", ["SYSIN"], 0.90),
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("编码转换", ["ALPHABETIC", "ASCII", "EBCDIC"], 0.85),
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("online", ["DFHCOMMAREA", "MAP"], 0.95),
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("SORT", ["SORT ON KEY"], 0.95),
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("MERGE", ["MERGE ON KEY"], 0.95),
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("编辑输出", ["WRITE AFTER", "WRITE BEFORE"], 0.80),
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("文件编成", ["ORGANIZATION IS"], 0.99),
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("替代索引", ["ALTERNATE RECORD KEY"], 0.99),
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]
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# ── 冲突解决规则 ─────────────────────────────────────────────────────────
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# 当 L1 匹配到多个分类时的消歧策略:
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# value = "file_count" → 取测试数更多的分类
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# value = "has_accumulator" → 取包含累加器的分类
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CONFLICT_RULES: dict[tuple[str, str], str] = {
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("マッチング", "キーブレイク"): "file_count",
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("編集処理", "項目チェック"): "file_count",
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("キーブレイク", "項目チェック(重複)"): "has_accumulator",
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}
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# ── 关键字检测 ───────────────────────────────────────────────────────────
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def detect_keyword(source: str) -> list[tuple[str, float, str]]:
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"""在 COBOL 源码中搜索 L1_RULES 定义的关键字，返回匹配结果。
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Args:
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source: COBOL 程序源码文本。
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Returns:
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list[tuple[str, float, str]]:
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每个元素为 (分类名称, 置信度, 匹配到的关键字原文)。
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"""
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results: list[tuple[str, float, str]] = []
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source_upper = source.upper()
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for category, keywords, confidence in L1_RULES:
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for kw in keywords:
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if kw in source_upper:
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results.append((category, confidence, kw))
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break # 同一分类只记录一次
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return results
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# ── 确信度计算 ───────────────────────────────────────────────────────────
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def compute_confidence(
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source: str,
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structure: dict[str, Any] | None = None,
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llm_result: dict[str, Any] | None = None,
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) -> dict[str, Any]:
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"""计算程序分类的确信度。
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优先级:
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1. L1 关键字命中，且最高置信度 >= 0.90 → 直接返回 L1 结果。
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2. LLM 结果存在 → 使用 LLM 的分类结果。
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3. 否则 → 返回 unknown。
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Args:
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source: COBOL 程序源码文本。
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structure: 可选的程序结构信息（暂未使用，保留扩展）。
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llm_result: 可选的 LLM 分类结果。
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预期格式: {"category": str, "confidence": float, ...}
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Returns:
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dict:
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- "category": str — 分类名称或 "unknown"
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- "confidence": float — 确信度 (0.0 ~ 1.0)
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- "source": str — 结果来源 ("l1" / "llm" / "unknown")
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- "matches": list — 匹配到的关键字详情
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"""
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# ── 1. L1 关键字检测 ──
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matches = detect_keyword(source)
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# 找出最高置信度的 L1 匹配
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if matches:
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best = max(matches, key=lambda m: m[1]) # (category, confidence, keyword)
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category, confidence, _ = best
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if confidence >= 0.90:
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return {
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"category": category,
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"confidence": confidence,
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"method": "keyword",
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"source": "l1",
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"features": [best[2]],
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"required_tests": [],
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"strategy_params": {"special_boundaries": [], "coverage_requirements": {"branch": 0.95, "paragraph": 1.0}},
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"matches": matches,
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}
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# ── 2. LLM 结果 ──
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if llm_result is not None:
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llm_category = llm_result.get("category", "unknown")
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llm_confidence = llm_result.get("confidence", 0.0)
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return {
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"category": llm_category,
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"confidence": llm_confidence,
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"method": "hybrid",
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"source": "llm",
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"features": [],
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"required_tests": [],
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"strategy_params": {"special_boundaries": [], "coverage_requirements": {"branch": 0.95, "paragraph": 1.0}},
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"matches": matches,
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}
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# ── 3. 未知 ──
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return {
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"category": "unknown",
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"confidence": 0.0,
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"method": "none",
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"source": "unknown",
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"features": [],
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"required_tests": [],
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"strategy_params": {"special_boundaries": [], "coverage_requirements": {"branch": 0.95, "paragraph": 1.0}},
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"matches": [],
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}
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