CNWei
00791809df
- 优化 WorkflowExecutor 与 Exchange支持 ExecutionEnv 资源注入。 - 实现 Session 级别连接复用与变量池内存镜像化，消除重复 I/O 开销。 - 引入 ChainMap 实现动态上下文切换，解决参数化变量与全局提取变量的优先级覆盖。 - 完善变量提取与断言逻辑，确保跨用例变量流转的可靠性。
129 lines
4.5 KiB
Python
129 lines
4.5 KiB
Python
#!/usr/bin/env python
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# coding=utf-8
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"""
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@author: chen wei
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@Software: PyCharm
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@contact: t6i888@163.com
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@file: models.py
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@date: 2024 2024/9/15 21:14
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@desc: 声明yaml用例格式
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"""
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import logging
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from typing import List, Any
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from pydantic import BaseModel, Field, ConfigDict
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logger = logging.getLogger(__name__)
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class HttpAction(BaseModel):
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method: str = Field(..., description="HTTP 请求方法: get, post, etc.")
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url: str = Field(..., description="接口路径或完整 URL")
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headers: dict[str, Any] | None = Field(default=None, description="HTTP 请求头")
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params: dict[str, Any] | None = Field(default=None, description="URL 查询参数")
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data: dict[str, Any] | None = None
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json_body: Any | None = Field(default=None, alias="json")
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timeout: int = 10
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files: dict[str, Any] | None = None
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model_config = ConfigDict(extra="allow", populate_by_name=True)
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class ApiActionModel(BaseModel):
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module: str = Field(..., alias="class", description="要调用的 API 类名")
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method: str = Field(..., description="类中的方法名")
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params: dict[str, Any] = Field(default_factory=dict, description="传给方法的参数")
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model_config = ConfigDict(populate_by_name=True)
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class ValidateItem(BaseModel):
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check: str = Field(..., description="要检查的字段或表达式")
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assert_method: str = Field(alias="assert", default="equals")
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expect: Any = Field(..., description="期望值")
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msg: str = Field(default="Assertion", description="断言描述")
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model_config = ConfigDict(populate_by_name=True)
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class RawSchema(BaseModel):
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title: str = Field(..., description="用例标题")
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epic: str | None = None
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feature: str | None = None
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story: str | None = None
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# 统一使用 action 字段承载业务逻辑 (Http 或 PO)
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action: dict[str, Any] = Field(description="请求内容或PO动作内容")
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extract: dict[str, List[Any]] | None = Field(
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default=None,
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description="变量提取表达式，格式: {变量名: [来源, 表达式, 索引]}"
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)
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validate_data: List[Any] = Field(
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default_factory=list,
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alias="validate",
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description="断言信息"
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)
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model_config = ConfigDict(extra="allow",
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populate_by_name=True, # 无论是在代码中用 api_class 还是在 YAML 中用 class 赋值，Pydantic 都能正确识别。
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arbitrary_types_allowed=True # 允许在模型中使用非 Pydantic 标准类型（如自定义类实例）
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) # 允许参数化等额外字段
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def is_po_mode(self) -> bool:
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"""判断是否为 PO 模式"""
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return "class" in self.action or "module" in self.action
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if __name__ == '__main__':
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# 模拟数据 1：标准请求模式
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raw_case_1 = {
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"title": "查询状态信息",
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"action": {
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"method": "get",
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"url": "/api/v1/info",
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"headers": {"User-Agent": "pytest-ai"},
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"json": {"User-Agent": "pytest-ai"}
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},
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"validate": [
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{"check": "status_code", "assert": "equals", "expect": 200, "msg": "响应码200"},
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{"check": "$.msg", "expect": "Success"}
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]
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}
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# 模拟数据 2：PO 模式 (反射调用)
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raw_case_2 = {
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"title": "用户登录测试",
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"action": {
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"class": "UserAPI",
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"method": "login",
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"params": {"user": "admin", "pwd": "123"}
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},
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"extract": {
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"token": ["json", "$.data.token", 0]
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}
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}
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print("--- 开始模型校验测试 ---\n")
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try:
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# 验证模式 1
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case1 = RawSchema(**raw_case_1)
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print(f"✅ 模式1 (Request) 校验通过: {case1.title}")
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print(f" http: {case1.action}")
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print(f" 断言规则数: {len(case1.validate_data)}\n")
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# 验证模式 2
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case2 = RawSchema(**raw_case_2)
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print(f"✅ 模式2 (PO Mode) 校验通过: {case2.title}")
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print(f" api: {case2.action}")
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print(f" 提取规则数: {len(case2.extract)}\n")
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# 验证非法数据（如：既没有 request 也没有 api_action 的情况可以在业务层进一步校验）
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# 这里演示 Pydantic 自动类型转换
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invalid_data = {"title": "错误用例", "action": {"url": "/api"}} # 缺少 method
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print("--- 预期失败测试 ---")
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RawSchema(**invalid_data)
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except Exception as e:
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print(f"❌ 预期内的校验失败: \n{e}")
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