基于大模型智能体的代码生成与优化技术

引言

在人工智能迅猛发展的今天，基于大模型的智能体正在彻底改变软件开发的范式。代码生成与优化作为AI在编程领域的核心应用，已经从简单的代码补全工具演变为能够理解复杂需求、生成高质量代码并持续优化的智能编程伙伴。本文将深入探讨大模型在代码生成与优化中的关键技术，通过详细的代码实例展示其实现原理，并分析这一技术对未来软件开发模式的深远影响。

随着GPT-4、CodeLlama等大型语言模型在代码理解能力上的突破，AI编程助手已经能够处理从简单函数实现到复杂系统设计的各种编程任务。然而，构建真正可靠的代码生成与优化系统仍面临着代码正确性、效率优化和系统集成等多方面的挑战。

大模型代码生成的技术基础

代码表示的现代化方法

现代代码生成系统建立在深度理解代码表示的基础上。传统的抽象语法树（AST）结合现代的代码嵌入技术，为模型提供了丰富的代码结构信息。

import ast
import torch
import torch.nn as nn
from tree_sitter import Language, Parser
import numpy as np

class CodeRepresentation:
    def __init__(self):
        # 初始化Tree-sitter解析器
        Language.build_library(
            'build/tree-sitter-languages.so',
            ['vendor/tree-sitter-python']
        )
        PYTHON_LANGUAGE = Language('build/tree-sitter-languages.so', 'python')
        self.parser = Parser()
        self.parser.set_language(PYTHON_LANGUAGE)
    
    def ast_to_structural_tokens(self, code):
        """将AST转换为结构化的token序列"""
        tree = self.parser.parse(bytes(code, 'utf8'))
        
        def traverse_node(node):
            tokens = []
            # 添加节点类型
            tokens.append(f"TYPE_{node.type}")
            
            # 处理子节点
            for child in node.children:
                tokens.extend(traverse_node(child))
            
            # 添加节点内容（如果是叶子节点）
            if len(node.children) == 0 and node.text:
                tokens.append(f"VAL_{node.text.decode('utf8')}")
            
            return tokens
        
        return traverse_node(tree.root_node)
    
    def create_code_embedding(self, code, model):
        """创建代码的向量表示"""
        structural_tokens = self.ast_to_structural_tokens(code)
        token_embeddings = []
        
        for token in structural_tokens:
            if hasattr(model, 'get_token_embedding'):
                embedding = model.get_token_embedding(token)
            else:
                # 备用嵌入方法
                embedding = self._hash_embedding(token)
            token_embeddings.append(embedding)
        
        # 聚合token嵌入得到代码嵌入
        if token_embeddings:
            code_embedding = torch.mean(torch.stack(token_embeddings), dim=0)
        else:
            code_embedding = torch.zeros(model.embedding_dim)
        
        return code_embedding
    
    def _hash_embedding(self, token):
        """基于哈希的简单嵌入方法"""
        hash_val = hash(token) % 10000
        embedding = torch.zeros(512)
        embedding[hash_val % 512] = 1.0
        return embedding

# 代码嵌入模型示例
class CodeEmbeddingModel(nn.Module):
    def __init__(self, vocab_size=10000, embedding_dim=512, hidden_dim=256):
        super().__init__()
        self.embedding_dim = embedding_dim
        self.token_embedding = nn.Embedding(vocab_size, embedding_dim)
        self.structural_encoder = nn.TransformerEncoder(
            nn.TransformerEncoderLayer(d_model=embedding_dim, nhead=8),
            num_layers=3
        )
        self.attention_pool = nn.MultiheadAttention(embedding_dim, num_heads=8)
    
    def forward(self, token_ids):
        embeddings = self.token_embedding(token_ids)
        encoded = self.structural_encoder(embeddings)
        # 使用注意力池化得到代码级别的表示
        query = torch.mean(encoded, dim=0, keepdim=True)
        attended, _ = self.attention_pool(query, encoded, encoded)
        return attended.squeeze(0)

代码生成的概率模型框架

大模型代码生成的核心是基于概率的序列生成，通过自回归方式预测下一个最可能的token。

import transformers
from transformers import GPT2LMHeadModel, GPT2Tokenizer
import torch.nn.functional as F

class CodeGenerationModel:
    def __init__(self, model_name="microsoft/CodeGPT-py"):
        self.tokenizer = GPT2Tokenizer.from_pretrained(model_name)
        self.model = GPT2LMHeadModel.from_pretrained(model_name)
        self.model.eval()
        
        # 添加代码特定的特殊token
        special_tokens = ['<code>', '</code>', '<function>', '</function>', '<class>', '</class>']
        self.tokenizer.add_tokens(special_tokens)
        self.model.resize_token_embeddings(len(self.tokenizer))
    
    def generate_code(self, prompt, max_length=500, temperature=0.8, top_k=50):
        """基于提示生成代码"""
        input_text = f"<code>\n{prompt}\n"
        input_ids = self.tokenizer.encode(input_text, return_tensors='pt')
        
        # 生成配置
        generation_config = {
            'max_length': len(input_ids[0]) + max_length,
            'temperature': temperature,
            'do_sample': True,
            'top_k': top_k,
            'pad_token_id': self.tokenizer.eos_token_id,
            'eos_token_id': self.tokenizer.encode('</code>')[0] if '</code>' in self.tokenizer.vocab else self.tokenizer.eos_token_id
        }
        
        with torch.no_grad():
            output_ids = self.model.generate(input_ids, **generation_config)
        
        generated_code = self.tokenizer.decode(output_ids[0], skip_special_tokens=False)
        # 提取生成的代码部分
        if '</code>' in generated_code:
            generated_code = generated_code.split('</code>')[0] + '</code>'
        
        return generated_code
    
    def beam_search_generation(self, prompt, num_beams=5, max_length=300):
        """使用束搜索生成更高质量的代码"""
        input_text = f"<code>\n{prompt}\n"
        input_ids = self.tokenizer.encode(input_text, return_tensors='pt')
        
        beam_output = self.model.generate(
            input_ids,
            max_length=len(input_ids[0]) + max_length,
            num_beams=num_beams,
            early_stopping=True,
            no_repeat_ngram_size=2,
            num_return_sequences=1,
            pad_token_id=self.tokenizer.eos_token_id
        )
        
        return self.tokenizer.decode(beam_output[0], skip_special_tokens=True)

# 高级代码生成器
class IntelligentCodeGenerator:
    def __init__(self):
        self.generation_model = CodeGenerationModel()
        self.representation_model = CodeRepresentation()
        
    def generate_with_retry(self, prompt, max_retries=3):
        """带重试机制的代码生成"""
        best_code = None
        best_score = -1
        
        for attempt in range(max_retries):
            try:
                # 使用不同的生成参数增加多样性
                temperature = 0.7 + attempt * 0.1
                generated_code = self.generation_model.generate_code(
                    prompt, temperature=temperature
                )
                
                # 评估生成代码的质量
                score = self.evaluate_code_quality(generated_code, prompt)
                
                if score > best_score:
                    best_score = score
                    best_code = generated_code
                    
            except Exception as e:
                print(f"生成尝试 {attempt + 1} 失败: {e}")
                continue
        
        return best_code, best_score
    
    def evaluate_code_quality(self, code, prompt):
        """评估生成代码的质量"""
        # 简单的质量评估启发式方法
        score = 0
        
        # 检查语法正确性
        try:
            ast.parse(self.extract_python_code(code))
            score += 0.3
        except:
            pass
        
        # 检查是否包含关键元素
        prompt_lower = prompt.lower()
        code_lower = code.lower()
        
        if "function" in prompt_lower and "def " in code_lower:
            score += 0.2
        if "class" in prompt_lower and "class " in code_lower:
            score += 0.2
        
        # 检查代码长度合理性
        lines = code.split('\n')
        if 5 <= len(lines) <= 50:
            score += 0.3
        
        return score
    
    def extract_python_code(self, generated_text):
        """从生成的文本中提取Python代码"""
        # 移除标记和说明文本
        lines = generated_text.split('\n')
        code_lines = []
        in_code = False
        
        for line in lines:
            if '<code>' in line:
                in_code = True
                continue
            if '</code>' in line:
                break
            if in_code and line.strip() and not line.strip().startswith('#'):
                code_lines.append(line)
        
        return '\n'.join(code_lines)

代码优化与重构技术

静态分析与优化建议

基于大模型的代码优化系统能够分析代码质量并提供具体的优化建议。

import radon
from radon.complexity import cc_visit
from radon.metrics import h_visit, mi_visit
import astor
import ast

class CodeOptimizer:
    def __init__(self):
        self.optimization_rules = self._load_optimization_rules()
    
    def analyze_code_quality(self, code):
        """全面分析代码质量"""
        analysis = {}
        
        try:
            # 计算圈复杂度
            complexity_results = cc_visit(code)
            analysis['cyclomatic_complexity'] = [
                {
                    'name': result.name,
                    'complexity': result.complexity,
                    'type': result.type
                }
                for result in complexity_results
            ]
            
            # 计算可维护性指数
            mi_results = mi_visit(code, True)
            analysis['maintainability_index'] = mi_results
            
            # Halstead度量
            halstead_results = h_visit(code)
            analysis['halstead_metrics'] = {
                'h1': halstead_results.total.h1,
                'h2': halstead_results.total.h2,
                'N1': halstead_results.total.N1,
                'N2': halstead_results.total.N2,
                'vocabulary': halstead_results.total.vocabulary,
                'length': halstead_results.total.length,
                'volume': halstead_results.total.volume,
                'difficulty': halstead_results.total.difficulty,
                'effort': halstead_results.total.effort
            }
            
        except Exception as e:
            print(f"代码质量分析错误: {e}")
            analysis['error'] = str(e)
        
        return analysis
    
    def suggest_optimizations(self, code):
        """提供代码优化建议"""
        suggestions = []
        
        # 解析AST进行分析
        try:
            tree = ast.parse(code)
            
            # 查找常见的优化机会
            suggestions.extend(self._find_loop_optimizations(tree))
            suggestions.extend(self._find_memory_optimizations(tree))
            suggestions.extend(self._find_performance_optimizations(tree))
            suggestions.extend(self._find_code_smells(tree))
            
        except SyntaxError as e:
            suggestions.append({
                'type': 'syntax_error',
                'description': f'语法错误: {e}',
                'priority': 'high'
            })
        
        return suggestions
    
    def _find_loop_optimizations(self, tree):
        """查找循环优化机会"""
        optimizations = []
        
        class LoopOptimizer(ast.NodeVisitor):
            def __init__(self):
                self.optimizations = []
            
            def visit_For(self, node):
                # 检查是否可以使用列表推导式
                if self._is_suitable_for_comprehension(node):
                    self.optimizations.append({
                        'type': 'loop_to_comprehension',
                        'description': '可以将for循环转换为列表推导式',
                        'line': node.lineno,
                        'priority': 'medium'
                    })
                
                # 检查循环中的重复计算
                self.generic_visit(node)
            
            def _is_suitable_for_comprehension(self, node):
                # 判断循环是否适合转换为推导式
                if len(node.body) != 1:
                    return False
                if not isinstance(node.body[0], ast.Expr):
                    return False
                if not isinstance(node.body[0].value, ast.Call):
                    return False
                if not isinstance(node.body[0].value.func, ast.Attribute):
                    return False
                if node.body[0].value.func.attr != 'append':
                    return False
                return True
        
        optimizer = LoopOptimizer()
        optimizer.visit(tree)
        return optimizer.optimizations
    
    def _find_memory_optimizations(self, tree):
        """查找内存使用优化机会"""
        optimizations = []
        
        class MemoryAnalyzer(ast.NodeVisitor):
            def __init__(self):
                self.optimizations = []
                self.large_data_structures = set()
            
            def visit_Assign(self, node):
                # 检查是否创建了大的数据结构
                if isinstance(node.value, ast.List) or isinstance(node.value, ast.Dict):
                    if len(node.value.elts) > 100:  # 大的列表或字典
                        self.optimizations.append({
                            'type': 'large_data_structure',
                            'description': '考虑使用生成器或分批处理大型数据结构',
                            'line': node.lineno,
                            'priority': 'medium'
                        })
                self.generic_visit(node)
        
        analyzer = MemoryAnalyzer()
        analyzer.visit(tree)
        return analyzer.optimizations
    
    def apply_optimizations(self, code, optimizations):
        """应用优化建议"""
        optimized_code = code
        applied_optimizations = []
        
        for opt in optimizations:
            if opt['priority'] in ['high', 'medium']:
                try:
                    # 这里实现具体的优化转换
                    if opt['type'] == 'loop_to_comprehension':
                        optimized_code = self._convert_loop_to_comprehension(optimized_code, opt)
                        applied_optimizations.append(opt)
                except Exception as e:
                    print(f"应用优化 {opt['type']} 时出错: {e}")
        
        return optimized_code, applied_optimizations
    
    def _convert_loop_to_comprehension(self, code, optimization):
        """将for循环转换为列表推导式"""
        # 简化的实现，实际中需要更复杂的AST转换
        lines = code.split('\n')
        line_num = optimization['line'] - 1
        
        if line_num < len(lines):
            # 在实际实现中，这里需要解析具体的循环结构并进行转换
            # 这里返回原始代码作为示例
            return code
        
        return code

智能重构系统

基于大模型的智能重构系统能够理解代码语义并提供安全的重构建议。

class IntelligentRefactoringSystem:
    def __init__(self):
        self.supported_refactorings = [
            'extract_method', 'rename_variable', 'inline_variable',
            'extract_class', 'move_method', 'introduce_parameter'
        ]
    
    def analyze_refactoring_opportunities(self, code):
        """分析代码中的重构机会"""
        opportunities = []
        
        try:
            tree = ast.parse(code)
            
            # 分析长方法
            opportunities.extend(self._find_long_methods(tree, code))
            
            # 分析重复代码
            opportunities.extend(self._find_duplicate_code(tree, code))
            
            # 分析过大类
            opportunities.extend(self._find_large_classes(tree, code))
            
            # 分析特征 envy
            opportunities.extend(self._find_feature_envy(tree, code))
            
        except Exception as e:
            print(f"重构分析错误: {e}")
        
        return opportunities
    
    def _find_long_methods(self, tree, code):
        """查找过长的方法"""
        long_methods = []
        
        class MethodLengthVisitor(ast.NodeVisitor):
            def __init__(self, source_code):
                self.source_lines = source_code.split('\n')
                self.long_methods = []
            
            def visit_FunctionDef(self, node):
                # 计算方法的行数
                start_line = node.lineno
                end_line = node.end_lineno if hasattr(node, 'end_lineno') else start_line
                method_length = end_line - start_line + 1
                
                if method_length > 20:  # 超过20行认为过长
                    self.long_methods.append({
                        'name': node.name,
                        'line': start_line,
                        'length': method_length,
                        'type': 'long_method',
                        'description': f'方法 {node.name} 过长 ({method_length} 行)，考虑提取子方法',
                        'priority': 'medium'
                    })
                
                self.generic_visit(node)
        
        visitor = MethodLengthVisitor(code)
        visitor.visit(tree)
        return visitor.long_methods
    
    def _find_duplicate_code(self, tree, code):
        """查找重复代码"""
        duplicates = []
        
        # 简化的重复代码检测
        # 实际实现中可以使用更复杂的算法，如基于AST的代码克隆检测
        lines = code.split('\n')
        line_hashes = {}
        
        for i, line in enumerate(lines):
            line_hash = hash(line.strip())
            if line_hash in line_hashes and len(line.strip()) > 20:  # 忽略短行
                duplicates.append({
                    'type': 'duplicate_code',
                    'description': f'第 {i+1} 行可能与第 {line_hashes[line_hash]+1} 行重复',
                    'line': i + 1,
                    'priority': 'low'
                })
            else:
                line_hashes[line_hash] = i
        
        return duplicates[:5]  # 返回前5个重复
    
    def perform_refactoring(self, code, refactoring_type, parameters):
        """执行具体的重构操作"""
        if refactoring_type == 'extract_method':
            return self._extract_method(code, parameters)
        elif refactoring_type == 'rename_variable':
            return self._rename_variable(code, parameters)
        else:
            raise ValueError(f"不支持的重构类型: {refactoring_type}")
    
    def _extract_method(self, code, parameters):
        """提取方法重构"""
        start_line = parameters['start_line']
        end_line = parameters['end_line']
        new_method_name = parameters['new_method_name']
        
        lines = code.split('\n')
        selected_lines = lines[start_line-1:end_line]
        
        # 分析选中的代码块，提取必要的参数
        extracted_code = '\n'.join(selected_lines)
        
        # 创建新方法
        indent = '    '
        new_method = f"{indent}def {new_method_name}():\n"
        new_method += '\n'.join([f"{indent*2}{line.strip()}" for line in selected_lines])
        new_method += f"\n\n{indent}# 调用新方法\n{indent}{new_method_name}()"
        
        # 替换原始代码
        new_lines = lines[:start_line-1] + [new_method] + lines[end_line:]
        
        return '\n'.join(new_lines)
    
    def _rename_variable(self, code, parameters):
        """重命名变量"""
        old_name = parameters['old_name']
        new_name = parameters['new_name']
        scope_lines = parameters.get('scope_lines', (1, len(code.split('\n'))))
        
        lines = code.split('\n')
        start_idx, end_idx = scope_lines[0]-1, scope_lines[1]
        
        # 在指定范围内重命名变量
        for i in range(start_idx, min(end_idx, len(lines))):
            # 简单的字符串替换，实际中需要基于AST进行精确的重命名
            lines[i] = lines[i].replace(old_name, new_name)
        
        return '\n'.join(lines)

测试生成与验证

智能测试用例生成

基于大模型的测试生成系统能够理解代码逻辑并生成全面的测试用例。

import unittest
import inspect
from io import StringIO
import sys

class IntelligentTestGenerator:
    def __init__(self):
        self.test_template = """import unittest

class Test{class_name}(unittest.TestCase):
{test_methods}

if __name__ == '__main__':
    unittest.main()
"""
    
    def generate_tests_for_function(self, function_code, function_name):
        """为函数生成测试用例"""
        # 分析函数签名和行为
        function_info = self._analyze_function(function_code, function_name)
        
        # 生成测试方法
        test_methods = self._generate_test_methods(function_info)
        
        # 组合测试类
        test_class = self.test_template.format(
            class_name=function_name.capitalize(),
            test_methods=test_methods
        )
        
        return test_class
    
    def _analyze_function(self, function_code, function_name):
        """分析函数特征"""
        # 执行函数代码以获取函数对象
        local_scope = {}
        try:
            exec(function_code, globals(), local_scope)
            func = local_scope.get(function_name)
            
            if func:
                # 获取函数签名
                signature = inspect.signature(func)
                parameters = list(signature.parameters.keys())
                
                # 分析函数文档字符串
                docstring = func.__doc__ or ""
                
                return {
                    'function': func,
                    'parameters': parameters,
                    'docstring': docstring,
                    'source_code': function_code
                }
        except Exception as e:
            print(f"分析函数时出错: {e}")
        
        return None
    
    def _generate_test_methods(self, function_info):
        """生成测试方法"""
        if not function_info:
            return "    pass"
        
        test_methods = []
        
        # 生成边界值测试
        test_methods.extend(self._generate_boundary_tests(function_info))
        
        # 生成异常测试
        test_methods.extend(self._generate_exception_tests(function_info))
        
        # 生成正常情况测试
        test_methods.extend(self._generate_normal_tests(function_info))
        
        return '\n'.join(test_methods)
    
    def _generate_boundary_tests(self, function_info):
        """生成边界值测试"""
        boundary_tests = []
        func_name = function_info['function'].__name__
        
        # 基于函数名的简单边界测试生成
        if 'divide' in func_name:
            boundary_tests.append(f"""    def test_divide_by_zero(self):
        with self.assertRaises(ZeroDivisionError):
            {func_name}(10, 0)""")
        
        if 'index' in func_name:
            boundary_tests.append(f"""    def test_index_out_of_range(self):
        with self.assertRaises(IndexError):
            {func_name}([1, 2, 3], 5)""")
        
        return boundary_tests
    
    def _generate_normal_tests(self, function_info):
        """生成正常情况测试"""
        normal_tests = []
        func_name = function_info['function'].__name__
        
        # 基于函数名的简单测试用例生成
        if 'add' in func_name:
            normal_tests.append(f"""    def test_add_positive_numbers(self):
        self.assertEqual({func_name}(2, 3), 5)""")
            
            normal_tests.append(f"""    def test_add_negative_numbers(self):
        self.assertEqual({func_name}(-2, -3), -5)""")
        
        elif 'factorial' in func_name:
            normal_tests.append(f"""    def test_factorial_zero(self):
        self.assertEqual({func_name}(0), 1)""")
            
            normal_tests.append(f"""    def test_factorial_positive(self):
        self.assertEqual({func_name}(5), 120)""")
        
        return normal_tests
    
    def execute_and_evaluate_tests(self, test_code, target_code):
        """执行测试并评估覆盖率"""
        # 创建测试运行器
        runner = unittest.TextTestRunner(stream=StringIO(), verbosity=2)
        
        # 动态加载测试模块
        test_module = self._create_dynamic_module('generated_tests', test_code)
        target_module = self._create_dynamic_module('target_code', target_code)
        
        # 发现并运行测试
        loader = unittest.TestLoader()
        test_suite = loader.loadTestsFromModule(test_module)
        
        result = runner.run(test_suite)
        
        # 计算简单的测试覆盖率（基于测试通过率）
        coverage = (result.testsRun - len(result.failures) - len(result.errors)) / result.testsRun if result.testsRun > 0 else 0
        
        return {
            'tests_run': result.testsRun,
            'failures': len(result.failures),
            'errors': len(result.errors),
            'coverage': coverage,
            'details': result
        }
    
    def _create_dynamic_module(self, module_name, code):
        """动态创建Python模块"""
        module = type(sys)('generated_tests')
        exec(code, module.__dict__)
        return module

系统集成与工程实践

完整的AI编程助手实现

class AICodingAssistant:
    def __init__(self):
        self.generator = IntelligentCodeGenerator()
        self.optimizer = CodeOptimizer()
        self.refactorer = IntelligentRefactoringSystem()
        self.test_generator = IntelligentTestGenerator()
        
        self.conversation_history = []
    
    def process_request(self, user_request):
        """处理用户编程请求"""
        self.conversation_history.append(f"用户: {user_request}")
        
        # 分析请求类型
        request_type = self._classify_request(user_request)
        
        if request_type == 'code_generation':
            response = self._handle_code_generation(user_request)
        elif request_type == 'code_optimization':
            response = self._handle_code_optimization(user_request)
        elif request_type == 'code_refactoring':
            response = self._handle_code_refactoring(user_request)
        elif request_type == 'test_generation':
            response = self._handle_test_generation(user_request)
        else:
            response = "抱歉，我无法处理这个类型的请求。"
        
        self.conversation_history.append(f"助手: {response}")
        return response
    
    def _classify_request(self, request):
        """分类用户请求"""
        request_lower = request.lower()
        
        if any(word in request_lower for word in ['写', '生成', '创建', '实现', '编写']):
            return 'code_generation'
        elif any(word in request_lower for word in ['优化', '改进', '提升', '加速']):
            return 'code_optimization'
        elif any(word in request_lower for word in ['重构', '重组', '整理']):
            return 'code_refactoring'
        elif any(word in request_lower for word in ['测试', '单元测试', 'test']):
            return 'test_generation'
        else:
            return 'unknown'
    
    def _handle_code_generation(self, request):
        """处理代码生成请求"""
        try:
            generated_code, score = self.generator.generate_with_retry(request)
            
            if score > 0.5:
                # 提供优化建议
                optimizations = self.optimizer.suggest_optimizations(
                    self.generator.extract_python_code(generated_code)
                )
                
                response = f"生成的代码:\n```python\n{generated_code}\n```\n"
                
                if optimizations:
                    response += "\n优化建议:\n"
                    for opt in optimizations[:3]:  # 只显示前3个建议
                        response += f"- {opt['description']} (优先级: {opt['priority']})\n"
                
                return response
            else:
                return "生成的代码质量较低，请尝试更具体的描述。"
                
        except Exception as e:
            return f"代码生成过程中出错: {e}"
    
    def _handle_code_optimization(self, request):
        """处理代码优化请求"""
        # 提取代码部分（简化实现）
        code_sections = self._extract_code_from_request(request)
        
        if not code_sections:
            return "请在请求中包含需要优化的代码。"
        
        response = "优化分析结果:\n\n"
        
        for i, code in enumerate(code_sections):
            # 分析代码质量
            quality_analysis = self.optimizer.analyze_code_quality(code)
            suggestions = self.optimizer.suggest_optimizations(code)
            
            response += f"代码段 {i+1} 分析:\n"
            response += f"- 可维护性指数: {quality_analysis.get('maintainability_index', 'N/A')}\n"
            
            if 'cyclomatic_complexity' in quality_analysis:
                avg_complexity = np.mean([c['complexity'] for c in quality_analysis['cyclomatic_complexity']])
                response += f"- 平均圈复杂度: {avg_complexity:.2f}\n"
            
            if suggestions:
                response += "- 主要建议:\n"
                for suggestion in suggestions[:2]:
                    response += f"  * {suggestion['description']}\n"
            
            response += "\n"
        
        return response
    
    def _extract_code_from_request(self, request):
        """从请求中提取代码（简化实现）"""
        # 在实际实现中，这里需要更复杂的代码提取逻辑
        code_blocks = []
        lines = request.split('\n')
        current_block = []
        in_code_block = False
        
        for line in lines:
            if '```' in line:
                if in_code_block and current_block:
                    code_blocks.append('\n'.join(current_block))
                    current_block = []
                in_code_block = not in_code_block
            elif in_code_block:
                current_block.append(line)
        
        return code_blocks

# 使用示例
if __name__ == "__main__":
    assistant = AICodingAssistant()
    
    # 测试代码生成
    response = assistant.process_request(
        "写一个Python函数计算斐波那契数列的第n项"
    )
    print(response)

未来展望与挑战

基于大模型的代码生成与优化技术正在快速发展，但仍面临多个重要挑战：

技术挑战

1. 代码正确性保证：如何确保生成的代码在逻辑和功能上的正确性
2. 复杂系统理解：处理大型、复杂代码库的架构理解
3. 领域特定知识：针对特定领域（如科学计算、嵌入式系统）的专业代码生成

工程挑战

1. 集成开发流程：如何将AI编程助手无缝集成到现有的开发工作流中
2. 版本控制与协作：在团队开发环境中管理AI生成的代码
3. 安全性与可靠性：防止生成存在安全漏洞的代码

未来发展方向

1. 多模态代码理解：结合代码、文档、图表等多种信息源
2. 实时协作编程：AI作为实时编程伙伴参与开发过程
3. 自我改进系统：能够从反馈中学习并改进代码生成质量

结论

基于大模型智能体的代码生成与优化技术正在重塑软件开发的未来。通过结合先进的自然语言处理、程序分析和机器学习技术，我们能够构建出真正理解开发者意图、生成高质量代码并持续优化的智能编程系统。

本文展示的技术框架和代码实例为构建这样的系统提供了坚实的基础。然而，要实现真正可靠的AI编程助手，我们还需要在代码正确性验证、系统架构理解和工程实践集成等方面进行持续的研究和开发。

随着技术的不断进步，AI编程助手将逐渐从简单的代码补全工具演变为真正的编程伙伴，与人类开发者共同创造更高质量、更可靠的软件系统。