NB-076
6b3f526b80
Refactor _resolve_matching_subtype to use an LLM agent for ambiguous cases instead of pure static rules: Architecture (3 layers): 1. Static deterministic rules: M:N→MxN, 1:N (WS-MAST/TRAN-KEY), 二段階, 混合 — high confidence, no LLM needed 2. LLM agent: ambiguous cases (N:1 vs 1:1, M:N→M vs M:N→N) - _MATCHING_SUBTYPE_AGENT_PROMPT with 5 subtypes - Calls existing hina.hina_agent._parse_llm_response for parsing - Minimum confidence threshold 0.4 to gate low-quality LLM output 3. Fallback: conservative defaults (M:N or 1:1) when LLM unavailable This follows the original architecture design: agent handles the hard classification problems that static analysis alone can't resolve. Regression: 745 passed (unchanged).
593 lines
21 KiB
Python
593 lines
21 KiB
Python
"""
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完整程序类型判定管道 — classify_program()
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流程:
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1. 并行: detect_keyword() + extract_structure()
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2. keyword confidence >= 90% -> 直接输出
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3. keyword 50-89% -> 规则引擎 + 确信度计算 + 矛盾回溯
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4. keyword < 50% -> LLM 辅助 + 规则引擎验证
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5. 输出最终 JSON
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"""
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from __future__ import annotations
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import logging
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from typing import Any
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from hina.classifier import detect_keyword
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from hina.confidence import compute_confidence_v2
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from hina.rule_engine.confusion_groups import resolve_confusion_pair
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from hina.rule_engine.contradiction import (
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CONTRADICTION_PAIRS,
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detect_contradictions,
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resolve_contradiction,
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)
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from cobol_testgen import extract_structure
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logger = logging.getLogger(__name__)
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# 所有可尝试的混淆对名称
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_PAIR_NAMES = [
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"matching_vs_keybreak",
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"dedup_vs_nodedup",
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"validation_vs_keybreak",
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"csv_merge_vs_split",
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"simple_vs_two_stage",
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"pure_vs_mixed",
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"division_50_25_100",
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"mn_output_mode",
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]
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# ── 内部工具 ──────────────────────────────────────────────────────────────────
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def _get_best_keyword_match(matches: list) -> dict | None:
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"""从 L1 关键字匹配结果中找出最佳匹配。
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Args:
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matches: detect_keyword() 返回的 list[tuple[str, float, str]]
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Returns:
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dict | None: {"category", "confidence", "keyword", "all_matches"}
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"""
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if not matches:
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return None
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best = max(matches, key=lambda m: m[1]) # (category, confidence, keyword)
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return {
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"category": best[0],
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"confidence": best[1],
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"keyword": best[2],
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"all_matches": matches,
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}
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def _compute_structure_match_score(structure: dict) -> int:
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"""计算结构匹配度评分 (0-5)，供 compute_confidence_v2 使用。"""
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return min(
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5,
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bool(structure.get("total_paragraphs", 0)) # 有段落
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+ bool(structure.get("file_count", 0)) # 有文件
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+ bool(len(structure.get("decision_points", []))) # 有决策点
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+ bool(structure.get("if_types", {}).get("total", 0)) # 有 IF
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+ bool(structure.get("branch_tree_obj") is not None), # 有分支树
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)
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def _build_structure_summary(structure: dict) -> dict:
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"""从完整结构中提取调试摘要。"""
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return {
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"paragraph_count": structure.get("total_paragraphs", 0),
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"file_count": structure.get("file_count", 0),
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"decision_count": len(structure.get("decision_points", [])),
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"has_call": structure.get("has_call", False),
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"has_divide": structure.get("has_divide", False),
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}
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def _build_keyword_result_for_v2(keyword_info: dict | None) -> dict:
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"""构建 compute_confidence_v2 所需的 keyword_result。"""
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if keyword_info:
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return {
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"base_confidence": keyword_info["confidence"],
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"match_count": len(keyword_info["all_matches"]),
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"category": keyword_info.get("category"),
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}
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return {"base_confidence": 0.0, "match_count": 0, "category": None}
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def _build_structure_features(structure: dict) -> dict:
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"""构建 compute_confidence_v2 所需的 structure_features。"""
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return {
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"structure_match_score": _compute_structure_match_score(structure),
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"total_paragraphs": structure.get("total_paragraphs", 0),
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}
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# ── 分路径逻辑 ────────────────────────────────────────────────────────────────
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def _path_keyword_direct(
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keyword_info: dict,
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structure: dict,
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) -> dict:
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"""路径 A: keyword confidence >= 90%, 直接输出。
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仍会计算 v2 确信度用于最终 validation，但结果来源标记为 "keyword"。
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"""
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keyword_result_v2 = _build_keyword_result_for_v2(keyword_info)
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structure_features = _build_structure_features(structure)
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v2_conf = compute_confidence_v2(
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keyword_result=keyword_result_v2,
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structure_features=structure_features,
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contradictions=[],
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resolution={"resolved_count": 0, "total_count": 0},
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)
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return {
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"category": keyword_info["category"],
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"confidence": v2_conf["confidence"],
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"needs_review": v2_conf["needs_review"],
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"method": "keyword",
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"source": "l1",
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"judgment": v2_conf["judgment"],
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"matches": keyword_info["all_matches"],
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"contradictions": [],
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"v2_confidence": v2_conf,
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"structure": _build_structure_summary(structure),
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}
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def _path_rule_engine(
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keyword_info: dict | None,
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structure: dict,
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) -> dict:
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"""路径 B: keyword 50-89%, 规则引擎 + 确信度计算 + 矛盾回溯。
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流程:
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1. 用 structure 特征构建 features dict
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2. 遍历所有混淆组解析器, 收集 resolved_types
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3. 检测矛盾并解决
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4. 确定最终分类
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5. 计算 4 因子确信度
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"""
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# 1. 结构特征直接作为 features
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features = dict(structure)
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# 2. 运行所有混淆组解析器
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resolved_types: dict[str, str] = {}
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resolved_confidences: dict[str, float] = {}
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for pair_name in _PAIR_NAMES:
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try:
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result = resolve_confusion_pair(features, pair_name)
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if result["resolved_type"] != "unknown" and result["confidence"] > 0:
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resolved_types[pair_name] = result["resolved_type"]
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resolved_confidences[pair_name] = result["confidence"]
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except Exception as e:
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logger.debug("[pipeline] 混淆对 %s 解析异常: %s", pair_name, e)
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features["resolved_types"] = resolved_types
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# 3. 矛盾检测与解决
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contradictions = detect_contradictions(features)
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resolution_map: dict[str, Any] = {
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"resolved_count": 0,
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"total_count": len(contradictions),
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}
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for c in contradictions:
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try:
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winner = resolve_contradiction(features, c)
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if winner:
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resolution_map[c.get("name", "unknown")] = winner
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resolution_map["resolved_count"] += 1
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except Exception as e:
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logger.debug("[pipeline] 矛盾解决异常: %s", e)
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# 4. 确定最终分类与基础置信度
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final_category = "unknown"
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final_base_confidence = 0.0
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# 优先采纳 keyword 判定
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if keyword_info:
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final_category = keyword_info["category"]
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final_base_confidence = keyword_info["confidence"]
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# 如果规则引擎有更高置信度的结果, 则采纳
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# 使用第一轮缓存的结果（M1: 消除冗余重复调用）
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best_resolved_type = None
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best_resolved_conf = 0.0
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for pair_name, rtype in resolved_types.items():
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cached_conf = resolved_confidences.get(pair_name, 0.0)
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if cached_conf > best_resolved_conf:
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best_resolved_conf = cached_conf
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best_resolved_type = rtype
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if best_resolved_type and best_resolved_conf > final_base_confidence:
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final_category = best_resolved_type
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final_base_confidence = best_resolved_conf
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# 5. 计算 4 因子确信度
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keyword_result_v2 = _build_keyword_result_for_v2(keyword_info)
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keyword_result_v2["base_confidence"] = final_base_confidence
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structure_features = _build_structure_features(structure)
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# 共识检测: L1 关键字分类与规则引擎最终分类一致时给予奖励
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kw_cat = keyword_info["category"] if keyword_info else None
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consensus_cat = kw_cat if (kw_cat and kw_cat == final_category) else None
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v2_confidence = compute_confidence_v2(
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keyword_result=keyword_result_v2,
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structure_features=structure_features,
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contradictions=contradictions,
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resolution=resolution_map,
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consensus_category=consensus_cat,
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)
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# 6. 组装结果
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return {
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"category": final_category,
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"confidence": v2_confidence["confidence"],
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"needs_review": v2_confidence["needs_review"],
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"method": "rule_engine",
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"source": "pipeline",
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"judgment": v2_confidence["judgment"],
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"matches": keyword_info["all_matches"] if keyword_info else [],
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"contradictions": contradictions,
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"contradiction_resolution": resolution_map,
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"resolved_types": resolved_types,
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"v2_confidence": v2_confidence,
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"structure": _build_structure_summary(structure),
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}
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def _path_llm_assisted(
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keyword_info: dict | None,
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structure: dict,
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llm: Any,
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) -> dict:
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"""路径 C: keyword < 50%, LLM 辅助 + 规则引擎验证。
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流程:
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1. 调用 classify_with_llm 获取 LLM 分类
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2. 规则引擎验证 LLM 结果
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3. 矛盾检测
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4. 确信度计算
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"""
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from hina.hina_agent import classify_with_llm
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# 1. LLM 分类
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llm_result = classify_with_llm(structure, llm)
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llm_category = llm_result.get("category", "unknown")
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llm_confidence = llm_result.get("confidence", 0.5)
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# 2. 规则引擎验证 LLM 分类
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features = dict(structure)
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validated_category = llm_category
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validated_confidence = llm_confidence
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for pair_name in _PAIR_NAMES:
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try:
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pair_result = resolve_confusion_pair(features, pair_name)
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if (pair_result["resolved_type"] != "unknown"
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and pair_result["confidence"] > validated_confidence):
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validated_category = pair_result["resolved_type"]
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validated_confidence = pair_result["confidence"]
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except Exception:
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continue
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# 3. 矛盾检测与解决 (M2: 消除硬编码 resolved_count=0)
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resolved_types: dict[str, str] = {}
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for pair_name in _PAIR_NAMES:
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try:
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rr = resolve_confusion_pair(features, pair_name)
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if rr["resolved_type"] != "unknown":
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resolved_types[pair_name] = rr["resolved_type"]
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except Exception:
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continue
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features["resolved_types"] = resolved_types
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contradictions = detect_contradictions(features)
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resolution_map: dict[str, Any] = {
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"resolved_count": 0,
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"total_count": len(contradictions),
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}
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for c in contradictions:
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try:
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winner = resolve_contradiction(features, c)
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if winner:
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resolution_map[c.get("name", "unknown")] = winner
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resolution_map["resolved_count"] += 1
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except Exception as e:
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logger.debug("[pipeline] Path C 矛盾解决异常: %s", e)
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# 4. 确信度计算
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keyword_result_v2 = _build_keyword_result_for_v2(keyword_info)
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keyword_result_v2["base_confidence"] = validated_confidence
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structure_features = _build_structure_features(structure)
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v2_confidence = compute_confidence_v2(
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keyword_result=keyword_result_v2,
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structure_features=structure_features,
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contradictions=contradictions,
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resolution=resolution_map,
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)
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return {
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"category": validated_category,
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"confidence": v2_confidence["confidence"],
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"needs_review": v2_confidence["needs_review"],
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"method": "llm",
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"source": "pipeline",
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"judgment": v2_confidence["judgment"],
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"matches": keyword_info["all_matches"] if keyword_info else [],
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"contradictions": contradictions,
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"llm_raw": llm_result,
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"v2_confidence": v2_confidence,
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"structure": _build_structure_summary(structure),
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}
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_MATCHING_SUBTYPE_AGENT_PROMPT = """你是一个 COBOL 迁移专家。请分析以下程序的键匹配模式，判断其匹配子类型。
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结构特征：
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- 文件数: {file_count}
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- 决策点: {decision_count}
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- IF 语句: {if_count}
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- 总分支: {total_branches}
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- 变量模式: {variable_patterns}
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源码中的关键变量：
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{key_vars}
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可选的匹配子类型（单选）：
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1. "1:1" — 1 个主文件对 1 个事务文件，一一对应
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2. "1:N" — 1 个主文件对 N 个事务文件
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3. "N:1" — N 个业务记录聚合成 1 个输出
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4. "M:N→M" — M:N 组合后按主键输出（输出 M 条）
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5. "M:N→N" — M:N 组合后按事务键输出（输出 N 条）
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请输出 JSON，不要添加其他文字：
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"""
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def _llm_subtype_inference(structure: dict, cobol_source: str, llm: Any) -> str | None:
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"""调用 LLM 推理匹配子类型。"""
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import re
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from hina.hina_agent import _parse_llm_response
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src_upper = cobol_source.upper()
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key_vars = sorted(set(re.findall(r'WS-[\w-]*KEY[A-Z0-9-]*', src_upper)))
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decision_points = structure.get("decision_points", [])
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if_count = sum(1 for dp in decision_points if dp.get("kind") == "IF")
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prompt = _MATCHING_SUBTYPE_AGENT_PROMPT.format(
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file_count=structure.get("file_count", 0),
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decision_count=len(decision_points),
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if_count=if_count,
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total_branches=structure.get("total_branches", 0),
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variable_patterns=str(structure.get("variable_patterns", {})),
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key_vars=", ".join(key_vars) if key_vars else "(无 KEY 变量)",
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)
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messages = [
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{"role": "system", "content": "你是一个 COBOL 匹配程序专家。只输出 JSON。"},
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{"role": "user", "content": prompt},
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]
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try:
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raw = llm.call(messages)
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parsed = _parse_llm_response(raw)
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subtype = parsed.get("subtype", "")
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confidence = parsed.get("confidence", 0.0)
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valid = {"1:1", "1:N", "N:1", "M:N→M", "M:N→N"}
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if subtype in valid and confidence >= 0.4:
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logger.info("[pipeline] LLM 子类型推理: %s (conf=%.2f, reason=%s)",
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subtype, confidence, parsed.get("reason", ""))
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return subtype
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except Exception as e:
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logger.debug("[pipeline] LLM 子类型推理失败: %s", e)
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return None
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# ── 主入口 ────────────────────────────────────────────────────────────────────
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# ── 匹配子类型解析 ──────────────────────────────────────────────────────────
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_MATCHING_SUBTYPE_RULES = [
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# (match_fn, subtype)
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# 按优先级从高到低排列
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]
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def _resolve_matching_subtype(
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result: dict,
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cobol_source: str,
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structure: dict,
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llm: Any = None,
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) -> dict:
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"""匹配程序的子类型区分后处理。
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使用分层策略:
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1. 静态规则处理确定性高的（M:N→MxN、1:N、混合、二段階）
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2. LLM agent 推理模棱两可的（N:1 vs 1:1、M:N→M vs M:N→N）
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3. 无 LLM 时回退保守默认值
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Args:
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result: classify_program 的返回结果。
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cobol_source: 原始 COBOL 源码。
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structure: extract_structure 的返回结构。
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llm: 可选的 LLM 客户端实例。
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Returns:
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更新后的 result，增加 "subtype" 字段。
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"""
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category = result.get("category", "")
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if "マッチング" not in category and "キーブレイク" not in category:
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return result # 非匹配程序不做子类型区分
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src_upper = cobol_source.upper()
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import re
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# 0. 二段階マッチング — 已在规则引擎中处理
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if "二段階" in category:
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result["subtype"] = "二段階"
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return result
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# 1. M:N→MxN 直積 — 特征: WRITE + WS-SAVE-KEY + 3 文件
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if structure.get("file_count", 0) >= 3 and 'WS-SAVE' in src_upper:
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result["subtype"] = "M:N→MxN"
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return result
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# 2. 混合匹配 (WS-PREV-KEY 存在)
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if 'WS-PREV-KEY' in src_upper:
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result["subtype"] = "混合"
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return result
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|
||
# 3. 检查键变量命名模式
|
||
key_vars = set(re.findall(r'WS-[\w-]*KEY[A-Z0-9-]*', src_upper))
|
||
|
||
# 不对称键名 → 1:N 或 N:1 (WS-MAST-KEY + WS-TRAN-KEY)
|
||
has_master = any('MAST' in k for k in key_vars)
|
||
has_tran = any('TRAN' in k for k in key_vars)
|
||
if has_master and has_tran:
|
||
result["subtype"] = "1:N"
|
||
return result
|
||
|
||
# ── 第 2 层: 静态规则+LLM 辅助 ──
|
||
# 多个键变量 + 多文件 → 可能是 M:N→M 或 M:N→N，需要 LLM 分辨
|
||
needs_llm = (
|
||
len(key_vars) >= 3 or
|
||
(len(key_vars) >= 2 and structure.get("file_count", 0) >= 2
|
||
and not has_master)
|
||
)
|
||
|
||
if needs_llm and llm is not None:
|
||
llm_subtype = _llm_subtype_inference(structure, cobol_source, llm)
|
||
if llm_subtype:
|
||
result["subtype"] = llm_subtype
|
||
return result
|
||
|
||
# ── 第 3 层: 回退 ──
|
||
# 多个键变量 → M:N（保守）
|
||
if len(key_vars) >= 3 and structure.get("file_count", 0) >= 2:
|
||
result["subtype"] = "M:N"
|
||
return result
|
||
|
||
# 对称键名 → 默认为 1:1
|
||
result["subtype"] = "1:1"
|
||
return result
|
||
|
||
|
||
def classify_program(cobol_source: str, llm: Any = None) -> dict:
|
||
"""完整程序类型判定管道。
|
||
|
||
流程:
|
||
1. 并行: detect_keyword() + extract_structure()
|
||
2. keyword confidence >= 90% -> 直接输出
|
||
3. keyword 50-89% -> 规则引擎 + 确信度计算 + 矛盾回溯
|
||
4. keyword < 50% -> LLM 辅助 + 规则引擎验证
|
||
5. 输出最终 JSON
|
||
|
||
Args:
|
||
cobol_source: COBOL 程序源码文本。
|
||
llm: 可选的 LLM 客户端实例。
|
||
在 keyword confidence < 50% 路径中用于 LLM 辅助分类。
|
||
若为 None 且 keyword < 50%, 则使用规则引擎兜底。
|
||
|
||
Returns:
|
||
dict: {
|
||
"category": str, # 程序分类名称
|
||
"confidence": float, # 综合确信度 (0.0 ~ 1.0)
|
||
"needs_review": bool, # 是否需要人工审核
|
||
"method": str, # "keyword" | "rule_engine" | "llm"
|
||
"source": str, # 结果来源: "l1" / "pipeline"
|
||
"judgment": str, # auto / review / manual / impossible
|
||
"matches": list, # L1 关键字匹配详情
|
||
"contradictions": list, # 矛盾列表
|
||
"v2_confidence": dict, # 4 因子确信度详情
|
||
"structure": dict, # 结构特征摘要（调试用）
|
||
}
|
||
|
||
Raises:
|
||
ValueError: 如果 cobol_source 为空或无效。
|
||
"""
|
||
if not cobol_source or not cobol_source.strip():
|
||
return {
|
||
"category": "unknown",
|
||
"confidence": 0.0,
|
||
"needs_review": True,
|
||
"method": "none",
|
||
"source": "error",
|
||
"judgment": "impossible",
|
||
"matches": [],
|
||
"contradictions": [],
|
||
"v2_confidence": {},
|
||
"structure": {},
|
||
}
|
||
|
||
# ── 第 1 步: 并行执行 keyword 检测和结构提取 ──
|
||
keyword_matches: list = []
|
||
structure: dict = {}
|
||
|
||
with ThreadPoolExecutor(max_workers=2) as executor:
|
||
future_keyword = executor.submit(detect_keyword, cobol_source)
|
||
future_structure = executor.submit(extract_structure, cobol_source)
|
||
|
||
for future in as_completed([future_keyword, future_structure]):
|
||
if future == future_keyword:
|
||
try:
|
||
keyword_matches = future.result()
|
||
except Exception as e:
|
||
logger.warning("[pipeline] detect_keyword 失败: %s", e)
|
||
elif future == future_structure:
|
||
try:
|
||
structure = future.result()
|
||
except Exception as e:
|
||
logger.warning("[pipeline] extract_structure 失败: %s", e)
|
||
|
||
# ── 第 2 步: 分析关键字结果, 确定路径 ──
|
||
keyword_info = _get_best_keyword_match(keyword_matches)
|
||
max_keyword_confidence = keyword_info["confidence"] if keyword_info else 0.0
|
||
|
||
logger.info(
|
||
"[pipeline] keyword matches=%d, max_confidence=%.2f, paragraphs=%d, files=%d",
|
||
len(keyword_matches),
|
||
max_keyword_confidence,
|
||
structure.get("total_paragraphs", 0),
|
||
structure.get("file_count", 0),
|
||
)
|
||
|
||
# ── 第 3 步: 根据确信度分路径 ──
|
||
|
||
# 路径 A: keyword >= 90% -> 直接输出
|
||
if max_keyword_confidence >= 0.90:
|
||
logger.info("[pipeline] 路径 A: keyword 高确信度 (%.2f)", max_keyword_confidence)
|
||
result = _path_keyword_direct(keyword_info, structure)
|
||
|
||
# 路径 B: keyword 50-89% -> 规则引擎
|
||
elif max_keyword_confidence >= 0.50:
|
||
logger.info("[pipeline] 路径 B: keyword 中确信度 (%.2f) -> 规则引擎", max_keyword_confidence)
|
||
result = _path_rule_engine(keyword_info, structure)
|
||
|
||
# 路径 C: keyword < 50% -> LLM 辅助
|
||
elif llm is not None:
|
||
logger.info("[pipeline] 路径 C: keyword 低确信度 (%.2f) -> LLM 辅助", max_keyword_confidence)
|
||
result = _path_llm_assisted(keyword_info, structure, llm)
|
||
|
||
# LLM 不可用: 使用规则引擎兜底
|
||
else:
|
||
logger.info("[pipeline] 路径 C(fallback): keyword 低确信度 (%.2f) -> 规则引擎兜底", max_keyword_confidence)
|
||
result = _path_rule_engine(keyword_info, structure)
|
||
result["method"] = "rule_engine_fallback"
|
||
|
||
# ── 第 4 步: 匹配子类型区分（仅对匹配/键中断程序）──
|
||
result = _resolve_matching_subtype(result, cobol_source, structure, llm=llm)
|
||
return result
|