565 lines
22 KiB
Python
565 lines
22 KiB
Python
import os
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import json
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import re
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import time
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import pandas as pd
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from typing import List, Dict, Optional, Tuple
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import numpy as np
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import faiss
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import openai
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from posthog import project_root
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from tree_sitter import Language, Parser
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import tree_sitter_cpp
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from openai import OpenAI
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# 配置指向 DashScope 的 OpenAI 兼容 endpoint
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DASHSCOPE_API_KEY = "sk-b51bf9fea0b3447896ce6d3a150f4a67"
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BASE_URL = "https://dashscope.aliyuncs.com/compatible-mode/v1"
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# 创建客户端（替代旧的 openai.api_key / openai.api_base）
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client = OpenAI(
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api_key=DASHSCOPE_API_KEY,
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base_url=BASE_URL
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)
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EMBEDDING_MODEL_NAME = "text-embedding-v4"
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KB_INDEX_PATH = "C:/Users\surface\Desktop/vector-knowledge/satellite_rag.faiss"
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KB_META_PATH = "C:/Users\surface\Desktop/vector-knowledge/satellite_rag_metadata.json"
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CPP_LANGUAGE = Language(tree_sitter_cpp.language())
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parser = Parser()
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parser.language = CPP_LANGUAGE
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# LLM 判断模型（仍可用 qwen-agent 或直接调 DashScope）
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LLM_MODEL_NAME = "qwen-max"
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# 输入输出
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INPUT_XLSX = ("")
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OUTPUT_JSON = "filtered_defects.json"
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Project_path = ""
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# 加载 FAISS 知识库
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print("Loading FAISS index...")
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index = faiss.read_index(KB_INDEX_PATH)
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with open(KB_META_PATH, "r", encoding="utf-8") as f:
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kb_meta = json.load(f)
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# 校验维度（v4 是 1024 维）
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assert index.d == 1024, f"FAISS 维度应为 1024，但实际为 {index.d}。请确认由 text-embedding-v4 构建！"
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assert len(kb_meta) == index.ntotal, "meta.json 条目数与 FAISS 向量数不一致！"
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print(f"Knowledge base loaded: {len(kb_meta)} entries, dim={index.d}")
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# ============================
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# 新增：通过 OpenAI 兼容 API 获取 Embedding
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# ============================
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def embed_text(text: str) -> np.ndarray:
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""" 使用新版 OpenAI 客户端调用 DashScope embedding """
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try:
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response = client.embeddings.create(
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model=EMBEDDING_MODEL_NAME, # "text-embedding-v4"
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input=text
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)
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# 新版 response 是 Pydantic 模型，不是 dict
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embedding = response.data[0].embedding # 注意：.data[0].embedding
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emb_np = np.array(embedding, dtype=np.float32).reshape(1, -1)
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return emb_np
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except Exception as e:
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print(f"Embedding API error: {e}")
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return np.zeros((1, 1024), dtype=np.float32)
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def get_function_context(file_path: str, line_number: int) -> Optional[Tuple[str, str]]:
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import chardet # 或 from charset_normalizer import from_path
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try:
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with open(file_path, 'rb') as f:
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raw_data = f.read()
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detected = chardet.detect(raw_data)
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encoding = detected['encoding']
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if encoding is None:
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encoding = 'utf-8'
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# 容错：某些检测结果如 'ascii' 可安全视为 utf-8
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if encoding.lower() in ('ascii', 'utf-8', 'utf-8-sig'):
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encoding = 'utf-8'
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elif 'gb' in encoding.lower():
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encoding = 'gb18030' # 兼容 gbk/gb2312
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else:
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encoding = 'utf-8' # 默认 fallback
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code = raw_data.decode(encoding, errors='replace')
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print("successfully decode the code text with " + encoding)
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except Exception as e:
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print(f"Failed to read {file_path}: {e}")
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return None
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tree = parser.parse(bytes(code, 'utf-8')) # 注意：tree-sitter 内部要求输入是 UTF-8 bytes！
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def find_function_node(node):
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if node.type == "function_definition":
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start_line = node.start_point[0] + 1
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end_line = node.end_point[0] + 1
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if start_line <= line_number <= end_line:
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func_name_node = node.child_by_field_name("declarator")
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if func_name_node:
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name = func_name_node.text.decode("utf-8").split("(")[0].strip().split()[-1]
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func_code = code[node.start_byte: node.end_byte]
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return name, func_code
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for child in node.children:
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res = find_function_node(child)
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if res:
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return res
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return None
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return find_function_node(tree.root_node)
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def get_fallback_context(file_path: str, line_number: int, window: int = 10) -> str:
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try:
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with open(file_path, "r", encoding="utf-8", errors="ignore") as f:
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lines = f.readlines()
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except Exception:
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return f"// Failed to read file around line {line_number}"
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start = max(0, line_number - 1 - window)
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end = min(len(lines), line_number - 1 + window + 1)
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snippet = "".join(lines[start:end])
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return f"// Context around line {line_number} (non-function):\n{snippet}"
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def retrieve_knowledge(query_text: str, top_k: int = 1) -> List[Dict]:
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emb = embed_text(query_text)
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D, I = index.search(emb, top_k)
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results = []
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for idx in I[0]:
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if 0 <= idx < len(kb_meta):
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results.append(kb_meta[idx])
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return results
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def retrieve_related_summaries(main_func_info: Dict, max_related: int = 3) -> Dict[str, str]:
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related = {"called_by": [], "calls": []}
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def fetch_summary(func_name, file_path):
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query = f"{func_name} in {file_path}"
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hits = retrieve_knowledge(query, top_k=1)
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if hits:
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hit = hits[0]
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return f"{hit['function_name']} in {hit['file_path']}: {hit.get('summary', 'No summary')}"
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return f"{func_name} in {file_path}: Summary not found"
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for item in main_func_info.get("called_by", [])[:max_related]:
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if isinstance(item, dict) and "function" in item and "file" in item:
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related["called_by"].append(fetch_summary(item["function"], item["file"]))
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for item in main_func_info.get("calls", [])[:max_related]:
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if isinstance(item, dict) and "function" in item and "file" in item:
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related["calls"].append(fetch_summary(item["function"], item["file"]))
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return related
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def get_urgency_score_for_A(defect_desc: str, reason: str) -> int:
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"""为高风险缺陷计算紧急修复分数"""
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prompt = f"""你是一位资深 C/C++ 静态分析专家和航天嵌入式系统安全工程师。
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以下是一个已被判定为高风险缺陷的问题，请根据其**严重性、可触发概率、后果影响（如崩溃、数据损坏、安全漏洞等）**，
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给出一个 0 到 100 的紧急修复分数（urgency score）：
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- 100 分：必然触发、导致系统崩溃或严重安全漏洞（如缓冲区溢出、除零、空指针解引用在关键路径）
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- 70～90 分：高概率触发，影响核心功能
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- 40～60 分：可能触发，影响次要功能
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- 0～30 分：极难触发，或后果轻微
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缺陷描述：
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{defect_desc}
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分析理由：
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{reason}
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请仅输出一个整数（0 到 100 之间），不要包含任何其他文字。"""
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try:
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response = client.chat.completions.create(
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model=LLM_MODEL_NAME,
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messages=[{"role": "user", "content": prompt}],
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temperature=0.0,
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max_tokens=5 # 足够输出一个数字
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)
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answer = response.choices[0].message.content.strip()
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# 使用正则提取第一个整数
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match = re.search(r'\d+', answer)
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if match:
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score = int(match.group())
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score = max(0, min(100, score)) # 限制在 0-100
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return score
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else:
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print(f"无法从模型响应中提取数字，使用默认值 50。原始响应: '{answer}'")
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return 50
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except Exception as e:
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print(f"获取 urgency_score 出错，使用默认值 50: {e}")
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return 50
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# ============================
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# 新增：影响域分析功能
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# ============================
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def retrieve_relevant_functions(query_text: str, top_k: int = 5) -> List[str]:
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"""
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根据查询文本，从知识库中检索最相关的函数 context_text 列表
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"""
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try:
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emb = embed_text(query_text)
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D, I = index.search(emb, top_k)
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contexts = []
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for idx in I[0]:
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if 0 <= idx < len(kb_meta):
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meta = kb_meta[idx]
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# 重建 context_text（与知识库构建时一致）
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context = (
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f"【实体类型】函数\n"
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f"【函数名】{meta.get('function_name', 'N/A')}\n"
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f"【所在文件】{os.path.basename(meta.get('file_path', 'N/A'))}\n"
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f"【功能摘要】{meta.get('summary', '无')}\n"
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f"【调用的函数】{', '.join(meta.get('calls', [])) if meta.get('calls') else '无'}\n"
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f"【被以下函数调用】{', '.join(meta.get('called_by', [])) if meta.get('called_by') else '无'}\n"
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f"【包含的头文件】{', '.join(meta.get('includes', [])) if meta.get('includes') else '无'}\n"
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f"{'-' * 40}"
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)
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contexts.append(context)
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return contexts
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except Exception as e:
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print(f"知识库检索出错: {e}")
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return []
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def get_functions_to_modify_with_knowledge(
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defect_desc: str,
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reason: str,
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file_path: str,
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line_number: int
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) -> List[str]:
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"""
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利用知识库检索上下文，让大模型返回需修改的函数列表
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"""
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# 构造查询文本：包含缺陷中的函数名、文件名等关键词
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query_text = f"{defect_desc}\n{reason}\n文件: {os.path.basename(file_path)}"
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# 检索相关函数上下文
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retrieved_contexts = retrieve_relevant_functions(query_text, top_k=5)
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knowledge_context_block = "\n".join(retrieved_contexts) if retrieved_contexts else "无相关函数知识库条目。"
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prompt = f"""你是一位资深 C/C++ 航天嵌入式软件工程师。请根据以下缺陷信息和**知识库检索到的函数上下文**，分析此缺陷影响了哪些功能实现和组件的工作。
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【缺陷描述】
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{defect_desc}
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【分析理由】
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{reason}
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【缺陷位置】
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文件: {file_path}
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行号: {line_number}
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【知识库检索结果】
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{knowledge_context_block}
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请严格按以下格式输出（示例）：
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ObtSunVecI
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InitAttEnv
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（若无，直接返回空）"""
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try:
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response = client.chat.completions.create(
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model=LLM_MODEL_NAME,
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messages=[{"role": "user", "content": prompt}],
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temperature=0.0,
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max_tokens=200
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)
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answer = response.choices[0].message.content.strip()
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if not answer or any(w in answer for w in ["无", "空", "没有", "未找到"]):
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return []
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functions = []
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for line in answer.splitlines():
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func = line.strip()
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if func and (func[0].isalpha() or func[0] == '_'):
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func = re.split(r'[^a-zA-Z0-9_]', func)[0]
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if func:
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functions.append(func)
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return functions
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except Exception as e:
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print(f"获取 functions_to_modify_with_knowledge 出错: {e}")
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return []
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def build_enhanced_prompt(
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defect_desc: str,
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file_path: str,
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line_number: int,
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main_context: str,
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main_knowledge: Optional[Dict],
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related_summaries: Optional[Dict],
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is_in_function: bool,
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) -> str:
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prompt = f"""你是一名资深 航天软件测试专家、C语言测试专家，请根据以下信息判断给出的缺陷告警是否为真实缺陷（True Positive），注意只关注当下源代码和知识库中的信息以确认该缺陷是否是真正的逻辑硬伤，对于可能导致潜在问题的非直接缺陷以及单纯的编码不规范问题进行忽视。
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【缺陷描述】
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{defect_desc}
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【缺陷位置】
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文件：{file_path}
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行号：{line_number}
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【代码上下文】
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{main_context}
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"""
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if is_in_function and main_knowledge:
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prompt += f"""【主函数知识库信息】
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- 函数名: {main_knowledge.get('function_name', 'N/A')}
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- 文件: {main_knowledge.get('file_path', 'N/A')}
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- 功能摘要: {main_knowledge.get('summary', 'N/A')}
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- 包含头文件: {', '.join(main_knowledge.get('includes', [])) or 'None'}
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"""
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if related_summaries:
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if related_summaries["called_by"]:
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prompt += "\n【调用此函数的关键函数摘要】\n" + "\n".join(related_summaries["called_by"])
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if related_summaries["calls"]:
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prompt += "\n\n【此函数调用的关键函数摘要】\n" + "\n".join(related_summaries["calls"])
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else:
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prompt += "注意：该缺陷位于非函数上下文（如全局变量、宏定义等），请谨慎判断。\n"
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prompt += """
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请严格按以下 JSON 格式输出，不要包含其他内容：
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{
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"is_real_defect": true 或 false,
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"reason": "简要说明原因",
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"risk_zone": ["影响域分析"],
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"suggestion": "修复建议"
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}
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"""
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return prompt
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def is_pure_style_issue(defect_desc: str) -> bool:
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"""
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快速判断缺陷描述是否仅为编码风格/规范问题（非逻辑缺陷）。
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若是，则可跳过后续源码分析和知识库检索，节省资源。
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返回 True 表示是纯风格问题（应舍弃），False 表示可能涉及逻辑，需进一步分析。
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"""
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style_prompt = f"""你是一名资深 C 语言航天软件测试专家。请判断以下静态分析工具报告的缺陷描述是否**仅涉及编码风格、格式、命名规范等非功能性问题**，而不涉及任何逻辑错误、内存安全、数值计算、状态机、控制流等实质性风险。
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【缺陷描述】
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{defect_desc}
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请严格按以下 JSON 格式输出：
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{{ "is_pure_style": true 或 false, "reason": "简要说明" }}"""
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try:
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completion = client.chat.completions.create(
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model=LLM_MODEL_NAME,
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messages=[{"role": "user", "content": style_prompt}],
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temperature=0.0,
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max_tokens=128
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)
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content = completion.choices[0].message.content
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# 尝试提取 JSON
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json_match = re.search(r"```(?:json)?\s*({.*?})\s*```", content, re.DOTALL)
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if json_match:
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result = json.loads(json_match.group(1))
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else:
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result = json.loads(content)
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return bool(result.get("is_pure_style", False))
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except Exception as e:
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print(f"Style filter LLM call failed: {e}. Treating as NOT pure style (proceed to full analysis).")
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return False # 出错时保守处理：进入完整分析
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def analyze_defect(defect_desc: str, file_path: str, line_number: int) -> Dict:
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# Step 1: 获取函数上下文
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func_info = get_function_context(file_path, line_number)
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if func_info:
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func_name, func_code = func_info
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main_context = f"// Function: {func_name}\n{func_code}"
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is_in_function = True
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# Step 2: 检索主函数知识
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query = f"{func_name} in {file_path}"
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main_knowledge_hits = retrieve_knowledge(query, top_k=1)
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main_knowledge = main_knowledge_hits[0] if main_knowledge_hits else None
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# Step 3: 获取相关调用摘要
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related_summaries = retrieve_related_summaries(main_knowledge) if main_knowledge else None
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else:
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# Fallback to raw context
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main_context = get_fallback_context(file_path, line_number)
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is_in_function = False
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main_knowledge = None
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related_summaries = None
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# Step 4: 构建增强 prompt
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prompt = build_enhanced_prompt(
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defect_desc=defect_desc,
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file_path=file_path,
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line_number=line_number,
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main_context=main_context,
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main_knowledge=main_knowledge,
|
||
related_summaries=related_summaries,
|
||
is_in_function=is_in_function
|
||
)
|
||
|
||
# Step 5: 调用 LLM
|
||
try:
|
||
completion = client.chat.completions.create(
|
||
model=LLM_MODEL_NAME, # "qwen-max"
|
||
messages=[{"role": "user", "content": prompt}],
|
||
temperature=0.0,
|
||
max_tokens=512
|
||
)
|
||
# 新版：通过属性访问，而非字典
|
||
content = completion.choices[0].message.content
|
||
|
||
# JSON 解析逻辑保持不变
|
||
json_match = re.search(r"```(?:json)?\s*({.*?})\s*```", content, re.DOTALL)
|
||
if json_match:
|
||
result = json.loads(json_match.group(1))
|
||
else:
|
||
result = json.loads(content)
|
||
|
||
# Step 6: 新增 - 为真实缺陷计算紧急分数
|
||
if result.get("is_real_defect") is True:
|
||
urgency_score = get_urgency_score_for_A(defect_desc, result.get("reason", ""))
|
||
result["urgency_score"] = urgency_score
|
||
|
||
# Step 7: 新增 - 为真实缺陷进行影响域分析
|
||
if(urgency_score>70):
|
||
affected_functions = get_functions_to_modify_with_knowledge(
|
||
defect_desc, result.get("reason", ""), file_path, line_number
|
||
)
|
||
result["affected_functions"] = affected_functions
|
||
else:
|
||
result["affected_functions"] =""
|
||
else:
|
||
result["urgency_score"] = 0 # 非真实缺陷分数为0
|
||
result["affected_functions"] = [] # 非真实缺陷无影响函数
|
||
|
||
return result
|
||
except Exception as e:
|
||
return {
|
||
"is_real_defect": None,
|
||
"reason": f"LLM call failed: {str(e)}",
|
||
"risk_points": [],
|
||
"suggestion": "大模型调用失败",
|
||
"urgency_score": 0, # 出错时分数为0
|
||
"affected_functions": [] # 出错时无影响函数
|
||
}
|
||
|
||
|
||
def process_defects_from_excel(input_xlsx: str, output_json: str):
|
||
print(f"Loading defects from {input_xlsx}...")
|
||
df = pd.read_excel(input_xlsx, engine="openpyxl")
|
||
|
||
if df.shape[1] < 13:
|
||
raise ValueError("Excel 至少需要 M 列（第13列）")
|
||
|
||
results = []
|
||
|
||
for idx, row in df.iterrows():
|
||
try:
|
||
file_path = Project_path + "/" + row.iloc[10] # K
|
||
line_str = row.iloc[11] # L
|
||
defect_desc = row.iloc[12] # M
|
||
|
||
if pd.isna(file_path) or pd.isna(defect_desc):
|
||
print(f"Skip row {idx + 2}: missing file or description")
|
||
continue
|
||
|
||
file_path = str(file_path).strip()
|
||
defect_desc = str(defect_desc).strip()
|
||
|
||
try:
|
||
line_number = int(float(line_str))
|
||
except (ValueError, TypeError):
|
||
print(f"Invalid line number at row {idx + 2}: {line_str}")
|
||
continue
|
||
|
||
print(f"Processing row {idx + 2}: {file_path}:{line_number}")
|
||
|
||
# >>>> 新增：快速风格过滤 <<<<
|
||
if is_pure_style_issue(defect_desc):
|
||
print(f" → Skipped (pure style issue): {defect_desc[:60]}...")
|
||
analysis = {
|
||
"is_real_defect": False,
|
||
"reason": "该问题仅为编码风格或规范问题，无实际逻辑风险。",
|
||
"risk_points": [],
|
||
"suggestion": "可忽略此类静态分析告警，或通过代码格式化工具统一处理。",
|
||
"urgency_score": 0, # 风格问题分数为0
|
||
"affected_functions": [] # 风格问题无影响函数
|
||
}
|
||
else:
|
||
# 原有完整分析流程
|
||
analysis = analyze_defect(defect_desc, file_path, line_number)
|
||
|
||
results.append({
|
||
"row_index": idx + 2,
|
||
"file_path": file_path,
|
||
"line_number": line_number,
|
||
"defect_description": defect_desc,
|
||
"analysis_result": analysis
|
||
})
|
||
|
||
# 可选：避免 API 限流（DashScope 免费版有 QPM 限制）
|
||
time.sleep(0.1)
|
||
|
||
except Exception as e:
|
||
print(f"Error processing row {idx + 2}: {e}")
|
||
results.append({
|
||
"row_index": idx + 2,
|
||
"file_path": str(row.iloc[10]) if not pd.isna(row.iloc[10]) else "",
|
||
"line_number": str(row.iloc[11]) if not pd.isna(row.iloc[11]) else "",
|
||
"defect_description": str(row.iloc[12]) if not pd.isna(row.iloc[12]) else "",
|
||
"analysis_result": {
|
||
"is_real_defect": None,
|
||
"reason": f"Unexpected error: {str(e)}",
|
||
"risk_points": [],
|
||
"suggestion": "处理过程中发生异常",
|
||
"urgency_score": 0, # 出错时分数为0
|
||
"affected_functions": []
|
||
}
|
||
})
|
||
|
||
with open(output_json, "w", encoding="utf-8") as f:
|
||
json.dump(results, f, indent=2, ensure_ascii=False)
|
||
|
||
print(f"\nCompleted! Results saved to {output_json}")
|
||
true_positives = sum(1 for r in results if r["analysis_result"].get("is_real_defect") is True)
|
||
false_positives = sum(1 for r in results if r["analysis_result"].get("is_real_defect") is False)
|
||
unknown = len(results) - true_positives - false_positives
|
||
|
||
# 统计紧急分数分布
|
||
high_urgency = sum(1 for r in results if r["analysis_result"].get("urgency_score", 0) >= 70)
|
||
medium_urgency = sum(1 for r in results if 40 <= r["analysis_result"].get("urgency_score", 0) < 70)
|
||
low_urgency = sum(1 for r in results if 0 < r["analysis_result"].get("urgency_score", 0) < 40)
|
||
|
||
# 统计影响函数数量
|
||
total_affected_functions = sum(len(r["analysis_result"].get("affected_functions", [])) for r in results)
|
||
defects_with_affected_functions = sum(1 for r in results if r["analysis_result"].get("affected_functions"))
|
||
|
||
print(f"统计：真实缺陷 {true_positives} 条，误报 {false_positives} 条，未知 {unknown} 条")
|
||
print(f"紧急程度分布：高紧急({high_urgency}条) 中紧急({medium_urgency}条) 低紧急({low_urgency}条)")
|
||
print(f"影响域分析：{defects_with_affected_functions} 个缺陷影响了 {total_affected_functions} 个函数")
|
||
|
||
|
||
# ============================
|
||
# 主程序
|
||
# ============================
|
||
if __name__ == "__main__":
|
||
process_defects_from_excel(INPUT_XLSX, OUTPUT_JSON) |