add cobol_testgen module
This commit is contained in:
hangshuo652
@@ -0,0 +1,308 @@
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"""AI智能体接口 — 基于DeepSeek的PROCEDURE DIVISION解析"""
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import json
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import os
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import re
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from pathlib import Path
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from .models import BrSeq, BrIf, BrEval, BrPerform, Assign, CallNode
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DEEPSEEK_API_KEY_ENV = "DEEPSEEK_API_KEY"
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DEEPSEEK_BASE_URL = "https://api.deepseek.com/v1"
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DEEPSEEK_MODEL = "deepseek-chat"
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PROMPT_FILE = Path(__file__).parent / "prompts" / "parse_proc_division.txt"
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def parse_proc_division_ai(proc_text: str, fields: list = None, spec_doc: str = ""):
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"""AI版PROCEDURE DIVISION解析：调用DeepSeek API，返回(branch_tree, assignments)."""
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api_key = os.environ.get(DEEPSEEK_API_KEY_ENV)
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if not api_key:
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raise NotImplementedError(
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f"AI agent requires {DEEPSEEK_API_KEY_ENV} environment variable"
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)
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prompt = _build_prompt(proc_text, fields)
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response_text = _call_llm(prompt, api_key)
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data = _extract_json(response_text)
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if not data:
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raise NotImplementedError("AI returned no parsable JSON")
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branch_tree = _json_to_tree(data.get("tree", {}))
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assignments = data.get("assignments", {})
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return branch_tree, assignments
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def _build_prompt(proc_text: str, fields: list = None) -> list[dict]:
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system = PROMPT_FILE.read_text(encoding="utf-8")
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fields_json = json.dumps(fields, ensure_ascii=False, indent=2) if fields else "[]"
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user = f"""## PROCEDURE DIVISION 源码
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```
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{proc_text}
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```
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## DATA DIVISION 字段列表
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```json
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{fields_json}
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```
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"""
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return [
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{"role": "system", "content": system},
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{"role": "user", "content": user},
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]
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def _call_llm(messages: list[dict], api_key: str) -> str:
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try:
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from openai import OpenAI
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except ImportError:
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raise NotImplementedError(
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"openai package not installed. Run: pip install openai"
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)
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client = OpenAI(api_key=api_key, base_url=DEEPSEEK_BASE_URL)
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response = client.chat.completions.create(
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model=DEEPSEEK_MODEL,
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messages=messages,
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temperature=0.1,
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max_tokens=8192,
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)
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return response.choices[0].message.content or ""
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def _extract_json(text: str) -> dict | None:
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stripped = text.strip()
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# Try extracting from markdown code block first
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m = re.search(r"```(?:json)?\s*\n?(.*?)\n?```", stripped, re.DOTALL)
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if m:
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stripped = m.group(1).strip()
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try:
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return json.loads(stripped)
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except json.JSONDecodeError:
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return None
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def _json_to_tree(data: dict):
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node_type = data.get("type", "seq")
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if node_type == "seq":
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node = BrSeq()
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for child_data in data.get("children", []):
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child = _json_to_tree(child_data)
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if child is not None:
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node.add(child)
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return node
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if node_type == "if":
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node = BrIf(data.get("condition", ""))
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node.true_seq = _json_to_tree(data.get("true_seq", {"type": "seq", "children": []}))
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node.false_seq = _json_to_tree(data.get("false_seq", {"type": "seq", "children": []}))
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return node
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if node_type == "eval":
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node = BrEval(data.get("subject", ""))
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for w in data.get("when_list", []):
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node.when_list.append((w.get("value", ""), _json_to_tree(w.get("seq", {"type": "seq", "children": []}))))
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node.other_seq = _json_to_tree(data.get("other_seq", {"type": "seq", "children": []}))
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node.has_other = data.get("has_other", False)
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return node
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if node_type == "perform":
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perf_type = data.get("perf_type", "para")
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kw = {"perf_type": perf_type}
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for k in ("condition", "target", "thru", "times",
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"varying_var", "varying_from", "varying_by"):
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if k in data:
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kw[k] = data[k]
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node = BrPerform(**kw)
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if "body_seq" in data:
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node.body_seq = _json_to_tree(data["body_seq"])
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return node
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if node_type == "assign":
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return Assign(
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target=data.get("target", ""),
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source_info=data.get("source_info", {}),
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)
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if node_type == "call":
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return CallNode(
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program_name=data.get("program_name", ""),
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using_params=data.get("using_params", []),
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)
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return None
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# ── LLM 路径生成 ──
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def llm_generate_all_paths(tree_root, fields) -> list | None:
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"""为整个控制流树生成 MC/DC 路径。返回 [(constraints, assignments), ...] 或 None。"""
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api_key = os.environ.get(DEEPSEEK_API_KEY_ENV)
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if not api_key:
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return None
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tree_json = _serialize_tree_for_llm(tree_root)
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if tree_json is None:
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return None
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level88_map = _extract_88_mapping(fields)
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messages = _build_path_prompt(tree_json, fields, level88_map)
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try:
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response = _call_llm(messages, api_key)
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data = _extract_json(response)
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if data and "paths" in data:
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return _parse_llm_paths(data["paths"])
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except Exception:
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pass
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return None
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def _serialize_tree_for_llm(node):
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if node is None:
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return None
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from .models import BrSeq, BrIf, BrEval, BrPerform, Assign, CallNode, ExitNode, GoTo
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if isinstance(node, BrSeq):
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children = []
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for child in node.children:
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s = _serialize_tree_for_llm(child)
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if s is not None:
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children.append(s)
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return {"type": "seq", "children": children} if children else None
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if isinstance(node, BrIf):
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return {
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"type": "if",
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"condition": node.condition,
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"true_seq": _serialize_tree_for_llm(node.true_seq) or {"type": "seq", "children": []},
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"false_seq": _serialize_tree_for_llm(node.false_seq) or {"type": "seq", "children": []},
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}
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if isinstance(node, BrEval):
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when_list = []
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for val, seq in node.when_list:
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s = _serialize_tree_for_llm(seq)
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when_list.append({"value": val, "seq": s or {"type": "seq", "children": []}})
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return {
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"type": "eval",
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"subject": node.subject,
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"when_list": when_list,
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"other_seq": _serialize_tree_for_llm(node.other_seq) or {"type": "seq", "children": []},
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"has_other": node.has_other,
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}
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if isinstance(node, BrPerform):
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result = {"type": "perform", "perf_type": node.perf_type}
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for attr in ("condition", "target", "thru", "times",
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"varying_var", "varying_from", "varying_by"):
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val = getattr(node, attr, None)
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if val is not None:
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result[attr] = val
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if node.body_seq:
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bs = _serialize_tree_for_llm(node.body_seq)
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if bs:
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result["body_seq"] = bs
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return result
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# Assign / CallNode / ExitNode / GoTo — 不影响路径生成，可省略
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return None
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def _extract_88_mapping(fields):
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mapping = {}
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for f in fields:
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if f.get('is_88'):
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mapping[f['name']] = {
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"parent": f['parent'],
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"value": f['value'],
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"pic_info": f.get('pic_info', {}),
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}
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return mapping
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def _build_path_prompt(tree_json, fields, level88_map):
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system = ("你是 COBOL 测试路径生成专家。"
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"请为给定的控制流树生成满足 MC/DC 覆盖的测试路径集。"
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"只输出 JSON，不要多余文字。")
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reduced_fields = []
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for f in fields:
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entry = {"name": f["name"], "pic": f.get("pic", "")}
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pi = f.get("pic_info", {})
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if pi:
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entry["pic_info"] = {
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"type": pi.get("type"), "digits": pi.get("digits"),
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"decimal": pi.get("decimal"), "length": pi.get("length"),
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}
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if f.get("is_88"):
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entry["is_88"] = True
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entry["value"] = f.get("value")
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entry["parent"] = f.get("parent")
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reduced_fields.append(entry)
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user = (
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"## 控制流树（JSON）\n\n"
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f"```json\n{json.dumps(tree_json, ensure_ascii=False, indent=2)}\n```\n\n"
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"## 字段定义\n\n"
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f"```json\n{json.dumps(reduced_fields, ensure_ascii=False, indent=2)}\n```\n\n"
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"## 要求\n"
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"1. 每个 IF/EVALUATE/PERFORM UNTIL 的每个分支至少被覆盖一次\n"
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"2. 复合条件（AND/OR/NOT）需要满足 MC/DC：每个叶条件的独立影响对\n"
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"3. 路径数尽量少（最小集优先）\n"
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"4. 88-level 条件名要展开为实际字段比较（如 CUST-VIP → WS-CUST-LEVEL='V'）\n"
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"5. 同一路径中的约束不能自相矛盾（同一字段不能同时等于 'A' 和等于 'B'）\n"
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"6. 数值边界值合理（>5000 → 5001, <100 → 99）\n"
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"7. AND 优先级高于 OR\n\n"
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"## 输出格式\n\n"
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"```json\n"
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"{\n"
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' "paths": [\n'
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" {\n"
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' "constraints": [\n'
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' {"field": "WS-AMOUNT", "op": ">", "value": "5000", "want_true": true}\n'
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" ],\n"
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' "assignments": {}\n'
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" }\n"
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" ]\n"
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"}\n"
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"```"
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)
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return [
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{"role": "system", "content": system},
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{"role": "user", "content": user},
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]
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def _parse_llm_paths(paths_data):
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result = []
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for p in paths_data:
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constraints = []
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for c in p.get("constraints", []):
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constraints.append((c["field"], c["op"], str(c["value"]), c["want_true"]))
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assignments = p.get("assignments", {})
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result.append((constraints, assignments))
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return result
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def resolve_constraints_ai(paths, fields=None, assignments=None):
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"""AI版约束推理（未来实现）"""
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raise NotImplementedError("AI agent not yet implemented")
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def enhance_metadata_ai(records, fields=None, spec_doc: str = ""):
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"""AI版测试用例元数据生成（未来实现）"""
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raise NotImplementedError("AI agent not yet implemented")
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def analyze_spec_ai(spec_doc: str = ""):
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"""AI版式样书解析（未来实现）"""
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raise NotImplementedError("AI agent not yet implemented")
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