517 lines
18 KiB
Plaintext
517 lines
18 KiB
Plaintext
import tree_sitter
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import faiss
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from openai import OpenAI
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from tree_sitter import Language, Parser
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import tree_sitter_c
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import tree_sitter_cpp
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import os
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import json
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import requests
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import chardet
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# Load C language
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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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CPP_EXTENSIONS = {'.c', '.cpp', '.h', '.hpp', '.tcc'}
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IGNORE_DIRS = {'build', 'cmake-build', '.git', 'vendor', 'lib', 'external', 'Debug'}
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from tree_sitter import Language, Parser
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def read_gbk_file(filepath):
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with open(filepath, 'rb') as f:
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raw_data = f.read()
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encoding = chardet.detect(raw_data)['encoding']
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if encoding.lower() in ('gbk', 'gb2312', 'cp936'):
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return raw_data.decode('gbk')
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else:
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return raw_data.decode('utf-8') # fallback
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def find_c_files(project_path):
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"""
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递归查找项目文件夹中的所有 C/C++ 文件
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"""
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c_files = []
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for root, dirs, files in os.walk(project_path):
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# 过滤需要忽略的目录
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dirs[:] = [d for d in dirs if d not in IGNORE_DIRS]
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for file in files:
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if any(file.endswith(ext) for ext in CPP_EXTENSIONS):
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full_path = os.path.join(root, file)
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c_files.append(full_path)
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return c_files
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def extract_functions_from_file(file_path):
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"""
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从单个文件中提取所有函数
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"""
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try:
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# 检测文件编码并读取
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with open(file_path, 'rb') as f:
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raw_data = f.read()
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encoding = chardet.detect(raw_data)['encoding']
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if encoding.lower() in ('gbk', 'gb2312', 'cp936'):
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text_str = raw_data.decode('gbk')
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else:
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text_str = raw_data.decode('utf-8')
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code_bytes = text_str.encode('utf-8')
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functions = extract_functions(code_bytes)
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# 为每个函数添加文件路径信息
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for func in functions:
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func['file_path'] = file_path
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func['file_name'] = os.path.basename(file_path)
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return functions
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except Exception as e:
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print(f"处理文件 {file_path} 时出错: {e}")
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return []
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def extract_functions(code_bytes: bytes):
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tree = parser.parse(code_bytes)
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root_node = tree.root_node
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functions = []
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def traverse(node):
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if node.type == 'function_definition':
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start = node.start_byte
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end = node.end_byte
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func_code = code_bytes[start:end].decode('utf-8')
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func_name = get_function_name(node, code_bytes)
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functions.append({
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'name': func_name,
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'code': func_code,
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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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})
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else:
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for child in node.children:
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traverse(child)
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traverse(root_node)
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return functions
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def find_declarator(node):
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"""
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递归查找 declarator 节点。
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在 C 语言中，函数名位于 declarator -> ... -> identifier 中。
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我们的目标是找到最内层的 declarator（通常是 function_declarator）。
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"""
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# 如果当前节点本身就是 declarator 类型，直接返回
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if node.type in ('declarator', 'function_declarator', 'pointer_declarator'):
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return node
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# 否则在其子节点中递归查找
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for child in node.children:
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# declarator 通常出现在 declaration 的第2个或之后的子节点中
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# 常见结构: [storage_class?, type, declarator, ...]
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result = find_declarator(child)
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if result is not None:
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return result
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return None
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def get_function_name(func_node, code_bytes: bytes):
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"""
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从 function_definition 节点中提取函数名。
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code_bytes: 原始源码的 bytes 对象（UTF-8 编码）
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"""
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declarator = None
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for child in func_node.children:
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if child.type == 'function_declarator':
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declarator = child
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break
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elif child.type == 'declaration':
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declarator = find_declarator(child)
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if declarator:
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break
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if declarator is None:
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return "<unknown>"
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def find_identifier(node):
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if node.type == 'identifier':
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return node
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for child in node.children:
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found = find_identifier(child)
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if found:
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return found
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return None
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ident_node = find_identifier(declarator)
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if ident_node:
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name_bytes = code_bytes[ident_node.start_byte:ident_node.end_byte]
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return name_bytes.decode('utf-8')
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else:
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return "<unnamed>"
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MAX_CHAR_LENGTH = 60000 # ～15k tokens
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import pandas as pd
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def load_review_rules(excel_path: str):
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"""
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从 Excel 加载评审规则。
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返回列表：[{"id": 1, "description": "..."}, ...]
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仅包含第3列为"是"的规则。
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"""
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df = pd.read_excel(excel_path, header=None, engine='openpyxl')
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# 假设：第0列=序号（可选），第1列=规则描述（B列），第2列=是否启用（C列）
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rules = []
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for idx, row in df.iterrows():
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# 兼容不同长度行
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if len(row) < 3:
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continue
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desc = str(row[1]).strip() if pd.notna(row[1]) else ""
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enabled = str(row[2]).strip() if pd.notna(row[2]) else ""
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if desc and enabled == "是":
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rule_id = str(row[0]).strip() if pd.notna(row[0]) else f"R{idx + 1}"
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rules.append({
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'id': rule_id,
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'description': desc
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})
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return rules
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def save_results_to_excel(results, output_path):
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"""
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将分析结果保存为 Excel 文件。
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:param results: List[Dict], 每个元素包含 keys:
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'function_name', 'rule_name', 'has_issue', 'explanation'
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:param output_path: str, 输出 Excel 路径，如 "audit_report.xlsx"
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"""
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df = pd.DataFrame(results, columns=[
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"文件名",
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"函数名",
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"规则名",
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"是否缺陷",
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"解释规则"
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])
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# 自动调整列宽（可选，提升可读性）
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with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
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df.to_excel(writer, index=False, sheet_name='静态分析结果')
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worksheet = writer.sheets['静态分析结果']
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for column in worksheet.columns:
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max_length = 0
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column_letter = column[0].column_letter
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for cell in column:
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try:
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if len(str(cell.value)) > max_length:
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max_length = len(str(cell.value))
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except:
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pass
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adjusted_width = min(max_length + 2, 80) # 最大列宽限制为80
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worksheet.column_dimensions[column_letter].width = adjusted_width
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print(f"分析结果已保存至: {output_path}")
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def split_long_function(func_code: str) -> list[str]:
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if len(func_code) <= MAX_CHAR_LENGTH:
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return [func_code]
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# 简单按行分片（更优方式：按语句块分割）
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lines = func_code.splitlines(keepends=True)
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chunks = []
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current = []
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current_len = 0
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for line in lines:
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if current_len + len(line) > MAX_CHAR_LENGTH and current:
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chunks.append(''.join(current))
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current = [line]
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current_len = len(line)
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else:
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current.append(line)
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current_len += len(line)
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if current:
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chunks.append(''.join(current))
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return chunks
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import os
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from openai import OpenAI
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client = OpenAI(
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base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
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api_key="sk-b51bf9fea0b3447896ce6d3a150f4a67"
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)
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RULE_BASED_PROMPT = """你是一名 C 语言安全审计专家。请根据以下特定编码或安全规则，严格审查提供的函数代码片段：
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【审查规则】
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{rule_description}
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【代码上下文】
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- 当前为函数 "{function_name}" 的第 {chunk_index}/{total_chunks} 片段
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- 若函数被分片，请综合所有片段判断；若仅提供一片，则基于此片判断
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【审查要求】
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- 如果此代码片段**明确违反**上述规则，请指出：违规位置、风险说明、修复建议；
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- 如果**未发现违规**，请回答："本片段未发现违反该规则的问题"；
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- 如果因**代码不完整无法判断**（如只看到函数开头），请回答："需结合完整函数判断"；
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- 回答必须简洁、基于事实，用中文，不超过50字。
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【回答要求】
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-请严格按以下 JSON 格式回答，不要包含任何额外文本、注释或 Markdown：
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{{
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"has_issue": true 或 false,
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"explanation": "简明解释为何存在或不存在该问题"
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}}
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【函数代码片段】
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c
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{code_chunk}
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"""
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def analyze_function_chunk_against_rule(
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client,
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func_name: str,
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code_chunk: str,
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rule_desc: str,
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chunk_index: int,
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total_chunks: int,
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model: str = "qwen-max"
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) -> str:
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prompt = RULE_BASED_PROMPT.format(
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rule_description=rule_desc,
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function_name=func_name,
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chunk_index=chunk_index,
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total_chunks=total_chunks,
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code_chunk=code_chunk
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)
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try:
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response = client.chat.completions.create(
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model=model,
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messages=[{"role": "user", "content": prompt}],
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temperature=0.1,
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max_tokens=500
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)
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return response.choices[0].message.content.strip()
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except Exception as e:
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return f"[API Error] {str(e)}"
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def analyze_function_against_rules(func_info: dict, rules: list, client, model: str = "qwen-max") -> dict:
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"""
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对单个函数应用所有启用的规则。
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若函数过长则分片，每片都送入同一条规则分析，最后合并结果。
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"""
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func_name = func_info['name']
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full_code = func_info['code']
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# 分片（复用之前的 split_long_function，但保留行信息更好）
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chunks = split_long_function(full_code) # 返回 list[str]
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total = len(chunks)
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rule_results = {}
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import json
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# 假设 analyze_function_chunk_against_rule 已按新 prompt 返回纯 JSON 字符串
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# 且 LLM 被强制要求只输出 JSON（无多余文本）
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for rule in rules:
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rule_id = rule['id']
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rule_desc = rule['description']
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chunk_results = [] # 存储每个 chunk 的结构化结果：{has_issue: bool, explanation: str}
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if total == 1:
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# 不分片
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raw_response = analyze_function_chunk_against_rule(
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client, func_name, chunks[0], rule_desc,
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chunk_index=1, total_chunks=1, model=model
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)
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try:
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res = json.loads(raw_response.strip())
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chunk_results.append({
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'has_issue': bool(res.get('has_issue', False)),
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'explanation': str(res.get('explanation', ''))
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})
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except (json.JSONDecodeError, AttributeError):
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# 解析失败时降级处理
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chunk_results.append({
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'has_issue': False,
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'explanation': f"[解析失败] 原始响应: {raw_response[:200]}..."
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})
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else:
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# 分片分析
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for i, chunk in enumerate(chunks):
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raw_response = analyze_function_chunk_against_rule(
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client, func_name, chunk, rule_desc,
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chunk_index=i + 1, total_chunks=total, model=model
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)
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try:
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res = json.loads(raw_response.strip())
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chunk_results.append({
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'has_issue': bool(res.get('has_issue', False)),
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'explanation': f"【片段{i + 1}】{res.get('explanation', '')}"
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})
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except (json.JSONDecodeError, AttributeError):
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chunk_results.append({
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'has_issue': False,
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'explanation': f"【片段{i + 1}】[解析失败] 原始响应: {raw_response[:200]}..."
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})
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# 合并分片结果：只要有一个片段存在缺陷，整体视为有缺陷
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final_has_issue = any(item['has_issue'] for item in chunk_results)
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final_explanation = "\n".join(item['explanation'] for item in chunk_results)
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rule_results[rule_id] = {
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'rule_description': rule_desc,
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'has_issue': final_has_issue, # 新增结构化字段
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'analysis': final_explanation # 保留原始解释用于报告
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}
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return rule_results
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def split_long_function(func_code: str, max_lines: int = 1600) -> list[str]:
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"""
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按行分片，尽量避免在 {} 中间切断（简单启发式）。
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"""
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lines = func_code.splitlines(keepends=True)
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if len(lines) <= max_lines:
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return [func_code]
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chunks = []
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current = []
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brace_depth = 0
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for line in lines:
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current.append(line)
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# 粗略跟踪花括号深度
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brace_depth += line.count('{') - line.count('}')
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if len(current) >= max_lines and brace_depth <= 0:
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# 在块结束处分片
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chunks.append(''.join(current))
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current = []
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brace_depth = 0
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if current:
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chunks.append(''.join(current))
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# 防御：万一 brace_depth 始终 >0，强制分片
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if not chunks:
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# fallback: 每 max_lines 行一切
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for i in range(0, len(lines), max_lines):
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chunks.append(''.join(lines[i:i + max_lines]))
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return chunks
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def main(project_path: str, rules_excel: str, output_report: str = "rule_based_report.md"):
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print("正在加载评审规则...")
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rules = load_review_rules(rules_excel)
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print(f"共加载 {len(rules)} 条启用的规则")
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print("正在读取 GBK 编码源文件...")
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print(f"正在扫描项目文件夹: {project_path}")
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c_files = find_c_files(project_path)
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print(f"找到 {len(c_files)} 个 C/C++ 文件")
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if not c_files:
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print("未找到任何 C/C++ 文件，请检查项目路径")
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return
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# 提取所有文件中的函数
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all_functions = []
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for file_path in c_files:
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print(f"正在处理文件: {file_path}")
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functions = extract_functions_from_file(file_path)
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all_functions.extend(functions)
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print(f" 找到 {len(functions)} 个函数")
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print(f"共找到 {len(all_functions)} 个函数")
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if not all_functions:
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print("未找到任何函数，分析结束")
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return
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# 初始化 OpenAI/Qwen 客户端
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client = OpenAI(
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base_url=os.getenv("QWEN_API_BASE", "https://dashscope.aliyuncs.com/compatible-mode/v1"),
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api_key=os.getenv("QWEN_API_KEY", "sk-b51bf9fea0b3447896ce6d3a150f4a67")
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)
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all_results = []
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for i, func in enumerate(all_functions, 1):
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print(f"\n分析函数 {i}/{len(all_functions)}: {func['name']} (来自 {func['file_name']})")
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rule_results = analyze_function_against_rules(func, rules, client)
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print(rule_results)
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all_results.append({
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'function': func,
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'rule_results': rule_results
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})
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# 生成 Excel 报告
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excel_rows = []
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for item in all_results:
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func_info = item['function']
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func_name = func_info['name']
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file_name = func_info['file_name']
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for rule_id, res in item['rule_results'].items():
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issue_flag = "是" if res.get('has_issue', False) else "否"
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excel_rows.append({
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"文件名": file_name,
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"函数名": func_name,
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"规则名": f"[{rule_id}] {res['rule_description']}",
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"是否缺陷": issue_flag,
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"解释规则": res['analysis']
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})
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||
|
||
# 调用保存 Excel 的函数
|
||
excel_output_path = output_report.replace('.md', '.xlsx')
|
||
save_results_to_excel(excel_rows, excel_output_path)
|
||
|
||
# 生成 Markdown 报告
|
||
with open(output_report, 'w', encoding='utf-8') as f:
|
||
f.write("# 基于规则的 C 函数静态分析报告\n\n")
|
||
f.write(f"**项目路径**: `{project_path}`\n\n")
|
||
f.write(f"**规则来源**: `{rules_excel}`\n\n")
|
||
f.write(f"**分析文件数**: {len(c_files)} 个\n\n")
|
||
f.write(f"**共分析函数**: {len(all_functions)} 个\n\n")
|
||
f.write(f"**启用规则数**: {len(rules)} 条\n\n---\n\n")
|
||
|
||
# 按文件分组显示结果
|
||
files_dict = {}
|
||
for item in all_results:
|
||
func = item['function']
|
||
file_path = func['file_path']
|
||
if file_path not in files_dict:
|
||
files_dict[file_path] = []
|
||
files_dict[file_path].append(item)
|
||
|
||
for file_path, items in files_dict.items():
|
||
f.write(f"## 文件: `{os.path.basename(file_path)}`\n\n")
|
||
f.write(f"**完整路径**: {file_path}\n\n")
|
||
|
||
for item in items:
|
||
func = item['function']
|
||
f.write(f"### 函数: `{func['name']}` (L{func['start_line']}-L{func['end_line']})\n\n")
|
||
f.write("```c\n")
|
||
snippet = func['code'][:1500] + ("..." if len(func['code']) > 1500 else "")
|
||
f.write(snippet)
|
||
f.write("\n```\n\n")
|
||
|
||
for rule_id, res in item['rule_results'].items():
|
||
f.write(f"#### 规则 [{rule_id}] {res['rule_description']}\n\n")
|
||
f.write(f"{res['analysis']}\n\n")
|
||
f.write("---\n\n")
|
||
|
||
print(f"\n分析完成！")
|
||
print(f"Excel 报告已保存至: {excel_output_path}")
|
||
print(f"Markdown 报告已保存至: {output_report}")
|
||
if __name__ == "__main__":
|
||
import os
|
||
os.environ["QWEN_API_BASE"] = "https://dashscope.aliyuncs.com/compatible-mode/v1"
|
||
os.environ["QWEN_API_KEY"] = ""
|
||
|
||
main(
|
||
project_path="",
|
||
rules_excel="审查规则.xlsx",
|
||
output_report="audit_report.md"
|
||
) |