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LlmStaticAnalyze.txt

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