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# 软件工程1.0：开发者即编码者
class TraditionalDeveloper:
    """
    角色定位：
    - 80%时间写代码
    - 15%时间调试
    - 5%时间设计

    核心技能：
    - 语法熟练度
    - API记忆
    - 手动调试
    - 文档查阅
    """

    def create_feature(self, requirement):
        # 1. 手写数据模型
        class User:
            def __init__(self, id, name, email):
                self.id = id
                self.name = name
                self.email = email

        # 2. 手写数据访问层
        class UserRepository:
            def get_by_id(self, id):
                # 手写SQL
                pass

        # 3. 手写API端点
        def user_endpoint(request):
            # 手写路由处理
            pass

        # 4. 手写测试
        def test_user():
            # 手写测试用例
            pass

        # ...所有代码都需要手动编写

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# 软件工程2.0：开发者即编排者
class AIAssistedDeveloper:
    """
    角色定位：
    - 30%时间设计架构
    - 20%时间编写提示词
    - 20%时间审查代码
    - 15%时间调试
    - 15%时间学习新知

    核心技能：
    - 系统设计能力
    - 提示工程
    - 代码审查
    - AI输出评估
    - 业务理解
    """

    def create_feature_with_ai(self, requirement):
        # 开发者：设计架构
        architecture = self.design_architecture(requirement)

        # AI：生成基础代码
        ai_generated = self.ai.generate({
            "task": "create user management feature",
            "architecture": architecture,
            "tech_stack": ["FastAPI", "SQLAlchemy", "Pydantic"],
            "requirements": [
                "RESTful API",
                "Input validation",
                "Error handling",
                "Unit tests"
            ]
        })

        # 开发者：审查并优化
        reviewed_code = self.review_and_refine(ai_generated)

        # 开发者：添加业务逻辑
        business_logic = self.implement_business_rules(
            reviewed_code,
            requirement.business_rules
        )

        # AI：生成测试
        tests = self.ai.generate_tests(business_logic)

        return {
            "code": business_logic,
            "tests": tests,
            "docs": self.ai.generate_docs(business_logic)
        }

    def design_architecture(self, requirement):
        """系统设计是开发者核心价值"""
        return {
            "layers": ["API", "Business Logic", "Data Access"],
            "patterns": ["Repository", "Dependency Injection"],
            "database": "PostgreSQL",
            "cache": "Redis",
            "security": ["JWT", "RBAC"]
        }

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// 角色转变对比
interface RoleEvolution {
  period: string;
  primary_role: string;
  key_skills: string[];
  daily_tasks: string[];
}

const evolution: RoleEvolution[] = [
  {
    period: "2020年前 (传统时代)",
    primary_role: "代码编写者",
    key_skills: [
      "语法熟练",
      "API记忆",
      "手写算法",
      "手动调试"
    ],
    daily_tasks: [
      "手写所有代码",
      "查找API文档",
      "调试编译错误",
      "编写样板代码"
    ]
  },
  {
    period: "2023-2024 (AI辅助初期)",
    primary_role: "代码审查者",
    key_skills: [
      "代码理解",
      "问题诊断",
      "AI工具使用",
      "代码质量判断"
    ],
    daily_tasks: [
      "编写核心逻辑",
      "审查AI生成代码",
      "优化性能",
      "处理边界情况"
    ]
  },
  {
    period: "2025-2026 (AI协作时代)",
    primary_role: "系统设计者",
    key_skills: [
      "系统架构",
      "提示工程",
      "业务理解",
      "AI编排",
      "质量把控"
    ],
    daily_tasks: [
      "设计系统架构",
      "编写精确提示",
      "审查AI输出",
      "整合AI生成模块",
      "确保系统质量"
    ]
  }
];

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# 提示工程的系统化方法

class PromptEngineering:
    """提示工程是2025年开发者的核心技能"""

    def __init__(self):
        self.templates = {}
        self.history = []

    def effective_prompt_structure(self, task: dict) -> str:
        """
        有效提示的结构化模板

        结构：
        1. Context (上下文)
        2. Task (任务)
        3. Requirements (要求)
        4. Constraints (约束)
        5. Examples (示例)
        6. Output Format (输出格式)
        """

        prompt = f"""
# Context
{task.get('context', 'We are building a web application using FastAPI and PostgreSQL')}

# Task
{task['task']}

# Requirements
{self._format_requirements(task.get('requirements', []))}

# Technical Constraints
- Tech Stack: {', '.join(task.get('tech_stack', []))}
- Code Style: {task.get('code_style', 'PEP 8 with type hints')}
- Error Handling: {task.get('error_handling', 'Comprehensive with custom exceptions')}

# Security Considerations
{self._format_security(task.get('security', []))}

# Output Format
{task.get('output_format', 'Complete, runnable code with imports')}

# Examples
{self._format_examples(task.get('examples', []))}
"""
        return prompt

    def code_generation_prompt(self, spec: dict) -> str:
        """代码生成专用提示"""

        return f"""
You are an expert {spec['language']} developer. Generate production-ready code for:

Feature: {spec['feature']}

Requirements:
{chr(10).join(f"- {r}" for r in spec['requirements'])}

Technical Specifications:
- Framework: {spec.get('framework', 'None specified')}
- Database: {spec.get('database', 'PostgreSQL')}
- Authentication: {spec.get('auth', 'JWT')}

Please provide:
1. Data models with full type annotations
2. Repository layer with error handling
3. Service layer with business logic
4. API endpoints with input validation
5. Unit tests for critical paths

Ensure:
- All code follows PEP 8
- Type hints throughout
- Docstrings for all public methods
- Proper exception handling
- Logging at appropriate levels
"""

    def code_review_prompt(self, code: str, focus_areas: list) -> str:
        """代码审查提示"""

        return f"""
Review the following code for potential issues:

Code:
```python
{code}

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# AI输出评估框架

class AICodeEvaluator:
    """AI生成代码的质量评估"""

    def __init__(self):
        self.criteria = {
            "correctness": {
                "weight": 0.4,
                "metrics": ["functional_correctness", "edge_cases"]
            },
            "security": {
                "weight": 0.25,
                "metrics": ["injection_vulnerabilities", "auth_issues"]
            },
            "performance": {
                "weight": 0.15,
                "metrics": ["time_complexity", "space_complexity"]
            },
            "maintainability": {
                "weight": 0.1,
                "metrics": ["code_readability", "naming_consistency"]
            },
            "testability": {
                "weight": 0.1,
                "metrics": ["test_coverage", "mockability"]
            }
        }

    def evaluate(self, code: str, requirements: dict) -> dict:
        """全面评估AI生成的代码"""

        scores = {}

        # 正确性检查
        scores['correctness'] = self._check_correctness(
            code,
            requirements.get('expected_behavior', [])
        )

        # 安全检查
        scores['security'] = self._check_security(code)

        # 性能分析
        scores['performance'] = self._analyze_performance(code)

        # 可维护性分析
        scores['maintainability'] = self._check_maintainability(code)

        # 可测试性分析
        scores['testability'] = self._check_testability(code)

        # 计算加权总分
        total_score = sum(
            scores[criterion] * self.criteria[criterion]['weight']
            for criterion in scores
        )

        return {
            "total_score": total_score,
            "breakdown": scores,
            "recommendation": self._make_recommendation(scores),
            "issues_found": self._collect_issues(scores)
        }

    def _check_correctness(self, code: str, expected_behaviors: list) -> float:
        """检查代码正确性"""

        # 1. 静态分析
        static_score = self._static_analysis(code)

        # 2. 运行测试
        test_score = self._run_tests(code, expected_behaviors)

        # 3. 边界情况检查
        edge_score = self._check_edge_cases(code)

        return (static_score + test_score + edge_score) / 3

    def _check_security(self, code: str) -> float:
        """安全检查"""

        issues = []

        # 检查常见安全问题
        security_patterns = {
            "sql_injection": [
                r'f"SELECT.*FROM.*WHERE',
                r'cursor\.execute.*%',
            ],
            "xss": [
                r'return.*HTML',
                r'render_template_string.*request',
            ],
            "hardcoded_secrets": [
                r'password\s*=\s*["\'].*["\']',
                r'API_KEY\s*=\s*["\'].*["\']',
            ],
            "insecure_deserialization": [
                r'pickle\.loads',
                r'yaml\.load\(unsafe',
            ]
        }

        for vuln_type, patterns in security_patterns.items():
            for pattern in patterns:
                import re
                if re.search(pattern, code):
                    issues.append({
                        "type": vuln_type,
                        "severity": "HIGH",
                        "pattern": pattern
                    })

        # 计算安全分数
        if not issues:
            return 1.0

        # 根据问题严重程度扣分
        score = 1.0
        for issue in issues:
            if issue['severity'] == 'CRITICAL':
                score -= 0.3
            elif issue['severity'] == 'HIGH':
                score -= 0.15

        return max(0, score)

    def _make_recommendation(self, scores: dict) -> str:
        """根据评分给出建议"""

        if all(score > 0.8 for score in scores.values()):
            return "代码质量优秀，可以接受"
        elif scores['security'] < 0.6:
            return "存在安全问题，必须修复"
        elif scores['correctness'] < 0.7:
            return "功能正确性存疑，需要人工测试"
        elif scores['performance'] < 0.6:
            return "性能问题，建议优化"
        else:
            return "代码可接受，建议审查后使用"

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# AI辅助开发的完整工作流

class AIDevelopmentWorkflow:
    """AI辅助开发的标准工作流"""

    def __init__(self):
        self.stages = [
            "requirement_analysis",
            "architecture_design",
            "code_generation",
            "code_review",
            "testing",
            "documentation",
            "integration"
        ]

    async def execute_workflow(self, user_story: dict):
        """执行完整的AI辅助开发流程"""

        workflow_result = {
            "stage_results": {},
            "ai_contributions": [],
            "human_decisions": []
        }

        # 阶段1：需求分析（人机协作）
        requirement_analysis = await self._analyze_requirements(user_story)
        workflow_result["stage_results"]["requirements"] = requirement_analysis

        # 阶段2：架构设计（人主导，AI辅助）
        architecture = await self._design_architecture(requirement_analysis)
        workflow_result["stage_results"]["architecture"] = architecture

        # 阶段3：代码生成（AI主导）
        generated_code = await self._generate_code(architecture)
        workflow_result["ai_contributions"].append("code_generation")

        # 阶段4：代码审查（人主导）
        review_result = await self._review_code(generated_code)
        workflow_result["stage_results"]["review"] = review_result

        # 阶段5：测试（AI生成，人验证）
        tests = await self._generate_tests(generated_code)
        test_result = await self._verify_tests(tests)
        workflow_result["stage_results"]["testing"] = test_result

        # 阶段6：文档生成（AI）
        docs = await self._generate_docs(generated_code, tests)
        workflow_result["ai_contributions"].append("documentation")

        # 阶段7：整合与验证（人主导）
        integration = await self._integrate_all({
            "code": generated_code,
            "tests": tests,
            "docs": docs
        })
        workflow_result["stage_results"]["integration"] = integration

        return workflow_result

    async def _analyze_requirements(self, user_story: dict) -> dict:
        """需求分析"""

        # AI分析用户故事
        ai_analysis = await self.ai_service.analyze({
            "user_story": user_story,
            "aspects": ["functional", "non_functional", "constraints"]
        })

        # 人工补充业务上下文
        human_context = {
            "business_rules": user_story.get("business_rules", []),
            "dependencies": user_story.get("dependencies", []),
            "edge_cases": user_story.get("edge_cases", [])
        }

        return {
            "ai_analysis": ai_analysis,
            "human_context": human_context,
            "refined_requirements": self._merge_requirements(
                ai_analysis,
                human_context
            )
        }

    async def _design_architecture(self, requirements: dict) -> dict:
        """架构设计"""

        # AI建议架构模式
        ai_suggestions = await self.ai_service.suggest_architecture({
            "requirements": requirements,
            "existing_system": self.get_current_architecture()
        })

        # 人工决策
        architecture = {
            "patterns": self.human_select_patterns(ai_suggestions),
            "tech_stack": self.human_validate_stack(ai_suggestions),
            "data_flow": self.human_design_data_flow(ai_suggestions)
        }

        return {
            "ai_suggestions": ai_suggestions,
            "human_decisions": architecture,
            "final_architecture": architecture
        }

    async def _generate_code(self, architecture: dict) -> dict:
        """代码生成"""

        # 按模块生成
        modules = {}

        for module_name, module_spec in architecture["modules"].items():
            # AI生成代码
            generated = await self.ai_service.generate_code({
                "module": module_name,
                "spec": module_spec,
                "architecture": architecture
            })

            # 记录生成信息
            modules[module_name] = {
                "code": generated,
                "generated_by": "AI",
                "confidence": generated.get("confidence", 0.8),
                "needs_review": True
            }

        return modules

    async def _review_code(self, code_dict: dict) -> dict:
        """代码审查"""

        review_results = {}

        for module_name, module_info in code_dict.items():
            code = module_info["code"]

            # AI自动审查
            ai_review = await self.ai_service.review_code({
                "code": code,
                "criteria": ["security", "performance", "style"]
            })

            # 人工深度审查
            human_review = {
                "business_logic_check": self.human_verify_logic(code),
                "edge_case_analysis": self.human_check_edge_cases(code),
                "performance_validation": self.human_measure_performance(code)
            }

            review_results[module_name] = {
                "ai_review": ai_review,
                "human_review": human_review,
                "approved": human_review["business_logic_check"]
            }

        return review_results

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# AI时代的代码审查

class ModernCodeReview:
    """人机协作的代码审查流程"""

    def __init__(self):
        self.checklist = {
            "ai_pre_check": [
                "语法正确性",
                "类型检查",
                "基础安全问题",
                "常见反模式"
            ],
            "human_deep_review": [
                "业务逻辑正确性",
                "架构适配性",
                "可维护性",
                "性能影响",
                "测试覆盖"
            ],
            "ai_post_check": [
                "与现有代码一致性",
                "文档完整性",
                "重构建议"
            ]
        }

    async def review_pull_request(self, pr: dict):
        """审查Pull Request的完整流程"""

        # 阶段1：AI预检查
        pre_check = await self._ai_pre_check(pr['changes'])

        if pre_check['critical_issues']:
            return {
                "status": "rejected",
                "reason": "AI预检查发现严重问题",
                "issues": pre_check['critical_issues']
            }

        # 阶段2：人工深度审查
        human_review = await self._human_deep_review({
            "changes": pr['changes'],
            "ai_findings": pre_check['findings']
        })

        # 阶段3：AI建议优化
        ai_suggestions = await self._ai_suggest_improvements({
            "code": pr['changes'],
            "review_feedback": human_review
        })

        # 阶段4：最终决策
        decision = self._make_decision({
            "pre_check": pre_check,
            "human_review": human_review,
            "ai_suggestions": ai_suggestions
        })

        return {
            "decision": decision,
            "feedback": {
                "pre_check": pre_check,
                "human_review": human_review,
                "ai_suggestions": ai_suggestions
            }
        }

    async def _ai_pre_check(self, changes: dict) -> dict:
        """AI预检查"""

        issues = []

        for file_name, file_changes in changes.items():
            # AI检查每个文件
            file_issues = await self.ai_service.check({
                "file": file_name,
                "changes": file_changes,
                "checks": [
                    "syntax_errors",
                    "type_errors",
                    "security_issues",
                    "code_smells"
                ]
            })

            # 分类问题
            for issue in file_issues:
                if issue['severity'] == 'CRITICAL':
                    issues.append(issue)

        return {
            "critical_issues": issues,
            "warnings": [i for i in issues if i['severity'] == 'WARNING'],
            "info": [i for i in issues if i['severity'] == 'INFO']
        }

    def _human_deep_review(self, context: dict) -> dict:
        """人工深度审查"""

        review = {
            "business_logic": self._review_business_logic(context),
            "architecture": self._review_architecture_fit(context),
            "performance": self._review_performance(context),
            "maintainability": self._review_maintainability(context),
            "testing": self._review_test_coverage(context)
        }

        # 综合评分
        scores = [v['score'] for v in review.values()]
        avg_score = sum(scores) / len(scores)

        return {
            "detailed_review": review,
            "average_score": avg_score,
            "recommendation": "approve" if avg_score > 0.7 else "request_changes"
        }

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# AI时代的质量保证

class AIAssuredQuality:
    """AI辅助的质量保证体系"""

    def __init__(self):
        self.quality_gates = {
            "code_generation": {
                "ai_confidence_threshold": 0.8,
                "human_review_required": True,
                "test_coverage_minimum": 0.8
            },
            "code_review": {
                "security_scan": "required",
                "performance_check": "required",
                "human_approval": "required_for_critical"
            },
            "deployment": {
                "automated_tests_pass": "required",
                "manual_verification": "for_high_risk"
            }
        }

    async def ensure_quality(self, artifact: dict, stage: str) -> dict:
        """确保AI产物的质量"""

        gates = self.quality_gates[stage]
        results = {}

        # 执行所有质量门检查
        for gate_name, gate_config in gates.items():
            result = await self._check_gate(artifact, gate_name, gate_config)
            results[gate_name] = result

            # 如果关键门未通过，立即停止
            if not result['passed'] and gate_config == "required":
                return {
                    "status": "failed",
                    "failed_at": gate_name,
                    "details": results
                }

        all_passed = all(r['passed'] for r in results.values())

        return {
            "status": "passed" if all_passed else "warning",
            "details": results
        }

    async def _check_gate(self, artifact: dict, gate_name: str, config: any) -> dict:
        """执行单个质量门检查"""

        if gate_name == "ai_confidence":
            return self._check_ai_confidence(artifact, config)
        elif gate_name == "human_review":
            return await self._check_human_review(artifact, config)
        elif gate_name == "security_scan":
            return await self._security_scan(artifact)
        elif gate_name == "test_coverage":
            return self._check_test_coverage(artifact, config)
        else:
            return {"passed": True, "message": f"Gate {gate_name} not implemented"}

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# AI辅助开发的常见陷阱和解决方案

class AITraps:
    """AI开发中的常见陷阱"""

    traps = {
        "过度依赖": {
            "症状": "完全接受AI输出，不审查",
            "风险": "安全漏洞、逻辑错误",
            "解决": "建立强制审查机制"
        },

        "提示模糊": {
            "症状": "提示词不明确",
            "风险": "AI生成不符合需求的代码",
            "解决": "使用结构化提示模板"
        },

        "忽略上下文": {
            "症状": "未提供足够的上下文信息",
            "风险": "AI生成的代码无法集成",
            "解决": "提供完整的系统上下文"
        },

        "缺少验证": {
            "症状": "不测试AI生成的代码",
            "风险": "运行时错误、边界情况bug",
            "解决": "自动化测试+人工测试"
        }
    }

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# AI辅助开发的最佳实践

class AIDevelopmentBestPractices:
    """AI辅助开发的最佳实践"""

    practices = {
        "prompt_engineering": {
            "1": "明确指定技术栈和框架",
            "2": "提供清晰的输入输出规范",
            "3": "包含安全要求",
            "4": "提供相关代码示例",
            "5": "说明性能约束"
        },

        "code_review": {
            "1": "AI预审查 + 人工深度审查",
            "2": "关注业务逻辑而非语法",
            "3": "检查安全性和性能",
            "4": "验证边界情况",
            "5": "评估可维护性"
        },

        "testing": {
            "1": "AI生成测试用例",
            "2": "人工验证测试覆盖",
            "3": "添加集成测试",
            "4": "性能基准测试",
            "5": "安全测试"
        },

        "documentation": {
            "1": "AI生成API文档",
            "2": "人工补充业务背景",
            "3": "添加架构决策记录",
            "4": "更新README",
            "5": "示例代码"
        }
    }