cobol-java-v3/hina/rule_engine/confusion_groups.py

"""混淆组判定规则引擎 — 8 个混淆对的化解函数。

每个函数接收 features dict，返回:
  {
    "resolved_type": str,
    "confidence": float,
    "evidence": list[str],
  }
"""

from __future__ import annotations


def resolve_matching_vs_keybreak(features: dict) -> dict:
    """区分「マッチング」与「キーブレイク」。

    规则:
      - IF 三路分支 (comparison ≥ 2) + SELECT 文件数 ≥ 2 → マッチング
      - IF 双路分支 (equality 为主) + WS-PREV-KEY 存在 + 累加器存在 → キーブレイク
    """
    if_types = features.get("if_types", {})
    total_ifs = if_types.get("total", 0)
    comparison_ifs = if_types.get("comparison", 0)
    equality_ifs = if_types.get("equality", 0)

    select_files = features.get("select_files", {})
    file_count = len(select_files) if isinstance(select_files, dict) else features.get("file_count", 0)

    variable_patterns = features.get("variable_patterns", {})
    has_prev_key = variable_patterns.get("has_prev_key", False)
    has_accumulator = variable_patterns.get("has_accumulator", False)

    evidence: list[str] = []

    # 规则 1: 三路分支 + 多文件 → マッチング
    if comparison_ifs >= 2 and file_count >= 2:
        evidence.append(f"三路 IF 分支 (comparison={comparison_ifs}) + SELECT 文件数 >=2 ({file_count}) → マッチング")
        return {"resolved_type": "マッチング", "confidence": 0.90, "evidence": evidence}

    # 规则 2: 双路 + WS-PREV-KEY + 累加器 → キーブレイク
    if total_ifs >= 1 and has_prev_key and has_accumulator:
        evidence.append(f"WS-PREV-KEY 存在 + 累加器存在 + IF 分支 → キーブレイク")
        return {"resolved_type": "キーブレイク", "confidence": 0.85, "evidence": evidence}

    # 补充规则: SELECT 文件数 >= 2 且 comparison 至少 1 → 倾向マッチング
    if file_count >= 2 and comparison_ifs >= 1:
        evidence.append(f"SELECT 文件数 >=2 + comparison IF >=1 → マッチング")
        return {"resolved_type": "マッチング", "confidence": 0.75, "evidence": evidence}

    # 回退: 无法明确判定
    evidence.append(f"特征不足: total_ifs={total_ifs}, comparison={comparison_ifs}, "
                     f"file_count={file_count}, has_prev_key={has_prev_key}, "
                     f"has_accumulator={has_accumulator}")
    return {"resolved_type": "unknown", "confidence": 0.0, "evidence": evidence}


def resolve_dedup_vs_nodedup(features: dict) -> dict:
    """区分「項目チェック(重複含む)」与「項目チェック(重複含まず)」。

    规则:
      - WS-PREV-KEY 存在 → 含重复
      - 无 WS-PREV-KEY → 不含重复
    """
    variable_patterns = features.get("variable_patterns", {})
    has_prev_key = variable_patterns.get("has_prev_key", False)
    evidence: list[str] = []

    if has_prev_key:
        evidence.append("WS-PREV-KEY 存在 → 含重复")
        return {"resolved_type": "項目チェック(重複含む)", "confidence": 0.90, "evidence": evidence}
    else:
        evidence.append("未检测到 WS-PREV-KEY → 不含重复")
        return {"resolved_type": "項目チェック(重複含まず)", "confidence": 0.85, "evidence": evidence}


def resolve_validation_vs_keybreak(features: dict) -> dict:
    """区分「編集処理(校验)」与「キーブレイク」。

    规则:
      - WS-ERR* 相关字段存在 → 校验 (validation)
      - WS-*CNT 累加计数器存在 → キーブレイク (key break)
    """
    variable_patterns = features.get("variable_patterns", {})
    has_error_flag = variable_patterns.get("has_error_flag", False)
    has_counter = variable_patterns.get("has_counter", False)
    evidence: list[str] = []

    if has_error_flag:
        evidence.append("WS-ERR* 错误字段存在 → 校验")
        return {"resolved_type": "編集処理(校验)", "confidence": 0.85, "evidence": evidence}

    if has_counter:
        evidence.append("WS-*CNT 计数器存在 → キーブレイク")
        return {"resolved_type": "キーブレイク", "confidence": 0.80, "evidence": evidence}

    evidence.append("既无错误字段也无计数器，无法判定")
    return {"resolved_type": "unknown", "confidence": 0.0, "evidence": evidence}


def resolve_csv_merge_vs_split(features: dict) -> dict:
    """区分 CSV 合并与拆分。

    规则:
      - STRING 语句存在 → 无换行 (合并, merge)
      - INSPECT REPLACING 存在 → 有换行 (拆分, split)
    """
    has_string = features.get("has_string", False)
    has_inspect = features.get("has_inspect", False)
    evidence: list[str] = []

    if has_string:
        evidence.append("STRING 语句存在 → CSV 合并 (无换行)")
        return {"resolved_type": "CSV合并", "confidence": 0.85, "evidence": evidence}

    if has_inspect:
        evidence.append("INSPECT REPLACING 存在 → CSV 拆分 (有换行)")
        return {"resolved_type": "CSV拆分", "confidence": 0.85, "evidence": evidence}

    evidence.append("既无 STRING 也无 INSPECT REPLACING")
    return {"resolved_type": "unknown", "confidence": 0.0, "evidence": evidence}


def resolve_simple_vs_two_stage(features: dict) -> dict:
    """区分「単純マッチング」与「二段階マッチング」。

    规则:
      - OPEN → CLOSE → 再 OPEN 模式 → 二级匹配
      - 其他顺序 → 简单匹配
    """
    open_pattern = features.get("open_pattern", "")
    evidence: list[str] = []

    if open_pattern == "open-close-open":
        evidence.append("OPEN→CLOSE→再OPEN 模式 → 二级匹配")
        return {"resolved_type": "二段階マッチング", "confidence": 0.90, "evidence": evidence}
    else:
        evidence.append(f"OPEN 模式为 '{open_pattern}' → 简单匹配")
        return {"resolved_type": "単純マッチング", "confidence": 0.80, "evidence": evidence}


def resolve_pure_vs_mixed(features: dict) -> dict:
    """区分「純粋マッチング」与「混合マッチング」。

    规则:
      - variable_patterns 中 has_switch 且 has_counter → 混合（隐含额外键比较）
      - 有 PERFORM 且 多文件 → 可能混合
      - 否则 → 纯粹匹配（低确信度，因无法静态确定有无额外键比较）
    """
    variable_patterns = features.get("variable_patterns", {})
    if_types = features.get("if_types", {})
    evidence: list[str] = []

    has_switch = variable_patterns.get("has_switch", False)
    has_counter = variable_patterns.get("has_counter", False)
    if_count = if_types.get("total", 0)

    if has_switch and has_counter and if_count >= 3:
        evidence.append("多个变量模式和 IF 分支 → 可能混合匹配")
        return {"resolved_type": "混合マッチング", "confidence": 0.70, "evidence": evidence}

    evidence.append("无明确混合特征 → 纯粹匹配（需数据验证）")
    return {"resolved_type": "unknown", "confidence": 0.0, "evidence": evidence}


def resolve_division_50_25_100(features: dict) -> dict:
    """区分 DIVIDE 被除数常量 50/25/100。

    从 features["divide_constants"] 列表中匹配已知常量。
    """
    divide_constants = features.get("divide_constants", [])
    evidence: list[str] = []

    if not isinstance(divide_constants, (list, tuple)):
        evidence.append("divide_constants 格式无效")
        return {"resolved_type": "unknown", "confidence": 0.0, "evidence": evidence}

    for c in divide_constants:
        if c in (50, 25, 100):
            evidence.append(f"DIVIDE 被除数 = {c}")
            return {"resolved_type": f"DIVIDE_{c}", "confidence": 0.95, "evidence": evidence}

    evidence.append(f"未匹配已知常量 (50/25/100)，当前值: {divide_constants}")
    return {"resolved_type": "unknown", "confidence": 0.0, "evidence": evidence}


def resolve_mn_output_mode(features: dict) -> dict:
    """判断 M:N 输出模式。

    规则:
      - 根据文件或记录数判断 M:N 关系
      - 返回 unknown 注明需数据验证
    """
    select_files = features.get("select_files", {})
    file_count = len(select_files) if isinstance(select_files, dict) else features.get("file_count", 0)
    evidence: list[str] = []

    # 尝试判断 M:N（从现有特征推断）
    select_count = len(select_files)
    total_branches = features.get("total_branches", 0)
    if select_count >= 2 and total_branches >= 3:
        evidence.append(f"SELECT={select_count}, 分支={total_branches} → 可能 M:N")
        return {"resolved_type": "M:N", "confidence": 0.65, "evidence": evidence}

    if file_count >= 3:
        evidence.append(f"文件数 {file_count} >= 3, 可能为 M:N 关系")
        return {"resolved_type": "M:N", "confidence": 0.60, "evidence": evidence}

    evidence.append("需数据验证确定 M:N 输出模式")
    return {"resolved_type": "unknown", "confidence": 0.0, "evidence": evidence}


# ── 调度表 ──────────────────────────────────────────────────────────────────

_RESOLVER_MAP = {
    "matching_vs_keybreak": resolve_matching_vs_keybreak,
    "dedup_vs_nodedup": resolve_dedup_vs_nodedup,
    "validation_vs_keybreak": resolve_validation_vs_keybreak,
    "csv_merge_vs_split": resolve_csv_merge_vs_split,
    "simple_vs_two_stage": resolve_simple_vs_two_stage,
    "pure_vs_mixed": resolve_pure_vs_mixed,
    "division_50_25_100": resolve_division_50_25_100,
    "mn_output_mode": resolve_mn_output_mode,
}


def resolve_confusion_pair(features: dict, pair_name: str) -> dict:
    """Dispatch to the correct function by pair_name."""
    resolver = _RESOLVER_MAP.get(pair_name)
    if resolver is None:
        return {
            "resolved_type": "unknown",
            "confidence": 0.0,
            "evidence": [f"未知混淆对名称: {pair_name}"],
        }
    return resolver(features)