2555 lines
161 KiB
Python
2555 lines
161 KiB
Python
"""
|
||
测试编排器模块
|
||
|
||
负责组合API解析器、API调用器、验证器和规则执行器，进行端到端的API测试
|
||
"""
|
||
|
||
import logging
|
||
import json
|
||
import time
|
||
import os # Added os for path operations
|
||
import re
|
||
from typing import Dict, List, Any, Optional, Union, Tuple, Type, ForwardRef
|
||
from enum import Enum
|
||
import datetime
|
||
import datetime as dt
|
||
from uuid import UUID
|
||
from dataclasses import asdict as dataclass_asdict, is_dataclass
|
||
import copy
|
||
|
||
from pydantic import BaseModel, Field, create_model, HttpUrl # Added HttpUrl for Literal type hint if needed
|
||
from pydantic.networks import EmailStr
|
||
from pydantic.types import Literal # Explicitly import Literal
|
||
|
||
from .input_parser.parser import InputParser, YAPIEndpoint, SwaggerEndpoint, ParsedYAPISpec, ParsedSwaggerSpec, ParsedAPISpec
|
||
from .api_caller.caller import APICaller, APIRequest, APIResponse, APICallDetail # Ensure APICallDetail is imported
|
||
from .json_schema_validator.validator import JSONSchemaValidator
|
||
from .test_framework_core import ValidationResult, TestSeverity, APIRequestContext, APIResponseContext, BaseAPITestCase
|
||
from .test_case_registry import TestCaseRegistry
|
||
from .utils import schema_utils
|
||
from .utils.common_utils import format_url_with_path_params
|
||
|
||
# 新增导入
|
||
from .stage_framework import BaseAPIStage, ExecutedStageResult, ExecutedStageStepResult, StageStepDefinition
|
||
from .stage_registry import StageRegistry
|
||
from .scenario_framework import BaseAPIScenario # ScenarioRegistry was incorrectly imported from here
|
||
from .scenario_registry import ScenarioRegistry # Corrected import for ScenarioRegistry
|
||
|
||
try:
|
||
from .llm_utils.llm_service import LLMService
|
||
except ImportError:
|
||
LLMService = None
|
||
logging.getLogger(__name__).info("LLMService 未找到，LLM 相关功能将不可用。")
|
||
|
||
_dynamic_model_cache: Dict[str, Type[BaseModel]] = {}
|
||
|
||
class ExecutedTestCaseResult:
|
||
"""存储单个APITestCase在其适用的端点上执行后的结果。"""
|
||
|
||
class Status(str, Enum):
|
||
"""单个测试用例的执行状态枚举"""
|
||
PASSED = "通过"
|
||
FAILED = "失败"
|
||
ERROR = "执行错误" # 指测试用例代码本身出错，而不是API验证失败
|
||
SKIPPED = "跳过" # 如果测试用例因某些条件被跳过执行
|
||
|
||
def __init__(self,
|
||
test_case_id: str,
|
||
test_case_name: str,
|
||
test_case_severity: TestSeverity,
|
||
status: Status,
|
||
validation_points: List[ValidationResult],
|
||
message: str = "", # 总体消息，例如执行错误时的错误信息
|
||
duration: float = 0.0):
|
||
self.test_case_id = test_case_id
|
||
self.test_case_name = test_case_name
|
||
self.test_case_severity = test_case_severity
|
||
self.status = status
|
||
self.validation_points = validation_points or []
|
||
self.message = message
|
||
self.duration = duration # 执行此测试用例的耗时
|
||
self.timestamp = datetime.datetime.now()
|
||
|
||
def to_dict(self) -> Dict[str, Any]:
|
||
message=""
|
||
if self.message:
|
||
message = self.message
|
||
else:
|
||
message= ";".join([vp.message for vp in self.validation_points])
|
||
return {
|
||
"test_case_id": self.test_case_id,
|
||
"test_case_name": self.test_case_name,
|
||
"test_case_severity": self.test_case_severity.value, # 使用枚举值
|
||
"status": self.status.value,
|
||
"message": message,
|
||
"duration_seconds": self.duration,
|
||
"timestamp": self.timestamp.isoformat(),
|
||
"validation_points": [vp.details if vp.details else {"passed": vp.passed, "message": vp.message} for vp in self.validation_points]
|
||
}
|
||
|
||
class TestResult: # 原来的 TestResult 被重构为 EndpointExecutionResult
|
||
"""
|
||
存储对单个API端点执行所有适用APITestCase后的整体测试结果。
|
||
(此类替换了旧的 TestResult 的角色，并进行了结构调整)
|
||
"""
|
||
class Status(str, Enum): # 这个枚举保持不变，但其含义现在是端点的整体状态
|
||
"""端点测试状态枚举"""
|
||
PASSED = "通过" # 所有关键测试用例通过
|
||
FAILED = "失败" # 任何一个关键测试用例失败
|
||
ERROR = "错误" # 测试执行过程中出现错误（非API本身错误，而是测试代码或环境）
|
||
SKIPPED = "跳过" # 如果整个端点的测试被跳过
|
||
PARTIAL_SUCCESS = "部分成功" # 一些非关键测试用例失败，但关键的通过
|
||
|
||
def __init__(self,
|
||
endpoint_id: str, # 通常是 method + path
|
||
endpoint_name: str, # API 的可读名称/标题
|
||
overall_status: Status = Status.SKIPPED, # 默认为跳过，后续根据测试用例结果更新
|
||
start_time: Optional[datetime.datetime] = None
|
||
):
|
||
self.endpoint_id = endpoint_id
|
||
self.endpoint_name = endpoint_name
|
||
self.overall_status = overall_status
|
||
self.executed_test_cases: List[ExecutedTestCaseResult] = []
|
||
self.start_time = start_time if start_time else datetime.datetime.now()
|
||
self.end_time: Optional[datetime.datetime] = None
|
||
self.error_message: Optional[str] = None # 如果整个端点测试出错，记录错误信息
|
||
self.message: Optional[str] = None
|
||
|
||
def add_executed_test_case_result(self, result: ExecutedTestCaseResult):
|
||
self.executed_test_cases.append(result)
|
||
|
||
def finalize_endpoint_test(self):
|
||
self.end_time = datetime.datetime.now()
|
||
# 根据所有 executed_test_cases 的状态和严重性来计算 overall_status
|
||
if not self.executed_test_cases and self.overall_status == TestResult.Status.SKIPPED : # 如果没有执行任何测试用例且状态仍为初始的SKIPPED
|
||
pass # 保持 SKIPPED
|
||
elif any(tc.status == ExecutedTestCaseResult.Status.ERROR for tc in self.executed_test_cases):
|
||
self.overall_status = TestResult.Status.ERROR
|
||
# 可以考虑将第一个遇到的ERROR的message赋给self.error_message
|
||
first_error = next((tc.message for tc in self.executed_test_cases if tc.status == ExecutedTestCaseResult.Status.ERROR), None)
|
||
if first_error:
|
||
self.error_message = f"测试用例执行错误: {first_error}"
|
||
else:
|
||
# 筛选出失败的测试用例
|
||
failed_tcs = [tc for tc in self.executed_test_cases if tc.status == ExecutedTestCaseResult.Status.FAILED]
|
||
if not failed_tcs:
|
||
if not self.executed_test_cases: # 如果没有执行任何测试用例但又不是SKIPPED，可能也算某种形式的错误或特殊通过
|
||
self.overall_status = TestResult.Status.PASSED # 或者定义一个"NO_CASES_RUN"状态
|
||
else:
|
||
self.overall_status = TestResult.Status.PASSED
|
||
else:
|
||
# 检查失败的测试用例中是否有CRITICAL或HIGH严重级别的
|
||
if any(tc.test_case_severity in [TestSeverity.CRITICAL, TestSeverity.HIGH] for tc in failed_tcs):
|
||
self.overall_status = TestResult.Status.FAILED
|
||
else: # 所有失败的都是 MEDIUM, LOW, INFO
|
||
self.overall_status = TestResult.Status.PARTIAL_SUCCESS
|
||
|
||
if not self.executed_test_cases and self.overall_status not in [TestResult.Status.SKIPPED, TestResult.Status.ERROR]:
|
||
# 如果没有执行测试用例，并且不是因为错误或明确跳过，这可能是一个配置问题或意外情况
|
||
self.overall_status = TestResult.Status.ERROR # 或者一个更特定的状态
|
||
self.error_message = "没有为该端点找到或执行任何适用的测试用例。"
|
||
|
||
|
||
@property
|
||
def duration(self) -> float:
|
||
if self.start_time and self.end_time:
|
||
return (self.end_time - self.start_time).total_seconds()
|
||
return 0.0
|
||
|
||
def to_dict(self) -> Dict[str, Any]:
|
||
data = {
|
||
"endpoint_id": self.endpoint_id,
|
||
"endpoint_name": self.endpoint_name,
|
||
"overall_status": self.overall_status.value,
|
||
"duration_seconds": self.duration,
|
||
"start_time": self.start_time.isoformat() if self.start_time else None,
|
||
"end_time": self.end_time.isoformat() if self.end_time else None,
|
||
"executed_test_cases": [tc.to_dict() for tc in self.executed_test_cases]
|
||
}
|
||
if self.error_message:
|
||
data["error_message"] = self.error_message
|
||
return data
|
||
|
||
class TestSummary:
|
||
"""测试结果摘要 (已更新以适应新的结果结构)"""
|
||
|
||
def __init__(self):
|
||
self.total_endpoints_defined: int = 0 # YAPI/Swagger中定义的端点总数
|
||
self.total_endpoints_tested: int = 0 # 实际执行了测试的端点数量 (至少有一个测试用例被执行)
|
||
|
||
self.endpoints_passed: int = 0
|
||
self.endpoints_failed: int = 0
|
||
self.endpoints_partial_success: int = 0
|
||
self.endpoints_error: int = 0
|
||
self.endpoints_skipped: int = 0 # 由于配置或过滤器，整个端点被跳过测试
|
||
|
||
self.total_test_cases_applicable: int = 0 # 所有端点上适用测试用例的总和
|
||
self.total_test_cases_executed: int = 0 # 所有端点上实际执行的测试用例总数
|
||
self.test_cases_passed: int = 0
|
||
self.test_cases_failed: int = 0
|
||
self.test_cases_error: int = 0 # 测试用例代码本身出错
|
||
self.test_cases_skipped_in_endpoint: int = 0 # 测试用例在端点执行中被跳过
|
||
|
||
# 新增：场景测试统计 -> 修改为 Stage 统计
|
||
self.total_stages_defined: int = 0
|
||
self.total_stages_executed: int = 0
|
||
self.stages_passed: int = 0
|
||
self.stages_failed: int = 0
|
||
self.stages_skipped: int = 0 # 如果 Stage 因为 is_applicable 返回 False 或其他原因被跳过
|
||
self.detailed_stage_results: List[ExecutedStageResult] = []
|
||
|
||
self.start_time = datetime.datetime.now()
|
||
self.end_time: Optional[datetime.datetime] = None
|
||
self.detailed_results: List[TestResult] = [] # 将存储新的 TestResult (EndpointExecutionResult) 对象
|
||
|
||
def add_endpoint_result(self, result: TestResult): # result 现在是新的 TestResult 类型
|
||
self.detailed_results.append(result)
|
||
|
||
if result.executed_test_cases or result.overall_status not in [TestResult.Status.SKIPPED, TestResult.Status.ERROR]: # 只有实际尝试了测试的端点才算tested
|
||
if not (len(result.executed_test_cases) == 0 and result.overall_status == TestResult.Status.ERROR and result.error_message and "没有为该端点找到或执行任何适用的测试用例" in result.error_message):
|
||
self.total_endpoints_tested +=1
|
||
|
||
if result.overall_status == TestResult.Status.PASSED:
|
||
self.endpoints_passed += 1
|
||
elif result.overall_status == TestResult.Status.FAILED:
|
||
self.endpoints_failed += 1
|
||
elif result.overall_status == TestResult.Status.PARTIAL_SUCCESS:
|
||
self.endpoints_partial_success +=1
|
||
elif result.overall_status == TestResult.Status.ERROR:
|
||
self.endpoints_error += 1
|
||
elif result.overall_status == TestResult.Status.SKIPPED: # 端点级别跳过
|
||
self.endpoints_skipped +=1
|
||
|
||
for tc_result in result.executed_test_cases:
|
||
self.total_test_cases_executed += 1 # 每个APITestCase算一次执行
|
||
if tc_result.status == ExecutedTestCaseResult.Status.PASSED:
|
||
self.test_cases_passed += 1
|
||
elif tc_result.status == ExecutedTestCaseResult.Status.FAILED:
|
||
self.test_cases_failed += 1
|
||
elif tc_result.status == ExecutedTestCaseResult.Status.ERROR:
|
||
self.test_cases_error +=1
|
||
elif tc_result.status == ExecutedTestCaseResult.Status.SKIPPED:
|
||
self.test_cases_skipped_in_endpoint +=1
|
||
|
||
def set_total_endpoints_defined(self, count: int):
|
||
self.total_endpoints_defined = count
|
||
|
||
def set_total_test_cases_applicable(self, count: int):
|
||
self.total_test_cases_applicable = count
|
||
|
||
# 新增：用于场景统计的方法 -> 修改为 Stage 统计方法
|
||
def add_stage_result(self, result: ExecutedStageResult):
|
||
self.detailed_stage_results.append(result)
|
||
# 只有实际执行的 Stage 才会计入 executed 计数器
|
||
# 如果一个Stage因为is_applicable=False而被跳过，它的executed_steps会是空的
|
||
if result.overall_status != ExecutedStageResult.Status.SKIPPED or result.executed_steps:
|
||
self.total_stages_executed += 1
|
||
|
||
if result.overall_status == ExecutedStageResult.Status.PASSED:
|
||
self.stages_passed += 1
|
||
elif result.overall_status == ExecutedStageResult.Status.FAILED:
|
||
self.stages_failed += 1
|
||
elif result.overall_status == ExecutedStageResult.Status.SKIPPED:
|
||
self.stages_skipped +=1
|
||
|
||
def set_total_stages_defined(self, count: int):
|
||
self.total_stages_defined = count
|
||
|
||
def finalize_summary(self):
|
||
self.end_time = datetime.datetime.now()
|
||
|
||
@property
|
||
def duration(self) -> float:
|
||
if not self.end_time:
|
||
return 0.0
|
||
return (self.end_time - self.start_time).total_seconds()
|
||
|
||
@property
|
||
def endpoint_success_rate(self) -> float:
|
||
if self.total_endpoints_tested == 0:
|
||
return 0.0
|
||
# 通常只把 PASSED 算作成功
|
||
return (self.endpoints_passed / self.total_endpoints_tested) * 100
|
||
|
||
@property
|
||
def test_case_success_rate(self) -> float:
|
||
if self.total_test_cases_executed == 0:
|
||
return 0.0
|
||
return (self.test_cases_passed / self.total_test_cases_executed) * 100
|
||
|
||
def to_dict(self) -> Dict[str, Any]:
|
||
data = {
|
||
"summary_metadata": {
|
||
"start_time": self.start_time.isoformat(),
|
||
"end_time": self.end_time.isoformat() if self.end_time else None,
|
||
"duration_seconds": f"{self.duration:.2f}",
|
||
},
|
||
"endpoint_stats": {
|
||
"total_defined": self.total_endpoints_defined,
|
||
"total_tested": self.total_endpoints_tested,
|
||
"passed": self.endpoints_passed,
|
||
"failed": self.endpoints_failed,
|
||
"partial_success": self.endpoints_partial_success,
|
||
"error": self.endpoints_error,
|
||
"skipped": self.endpoints_skipped,
|
||
"success_rate_percentage": f"{self.endpoint_success_rate:.2f}",
|
||
},
|
||
"test_case_stats": {
|
||
"total_applicable": self.total_test_cases_applicable, # 计划执行的测试用例总数
|
||
"total_executed": self.total_test_cases_executed, # 实际执行的测试用例总数
|
||
"passed": self.test_cases_passed,
|
||
"failed": self.test_cases_failed,
|
||
"error_in_execution": self.test_cases_error,
|
||
"skipped_during_endpoint_execution": self.test_cases_skipped_in_endpoint,
|
||
"success_rate_percentage": f"{self.test_case_success_rate:.2f}",
|
||
|
||
},
|
||
"detailed_results": [result.to_dict() for result in self.detailed_results]
|
||
}
|
||
# 新增：将场景测试结果添加到字典 -> 修改为 Stage 测试结果
|
||
data["stage_stats"] = {
|
||
"total_defined": self.total_stages_defined,
|
||
"total_executed": self.total_stages_executed,
|
||
"passed": self.stages_passed,
|
||
"failed": self.stages_failed,
|
||
"skipped": self.stages_skipped,
|
||
"success_rate_percentage": f"{(self.stages_passed / self.total_stages_executed * 100) if self.total_stages_executed > 0 else 0:.2f}"
|
||
}
|
||
data["detailed_stage_results"] = [res.to_dict() for res in self.detailed_stage_results]
|
||
return data
|
||
|
||
def to_json(self, pretty=True) -> str:
|
||
indent = 2 if pretty else None
|
||
return json.dumps(self.to_dict(), indent=indent, ensure_ascii=False)
|
||
|
||
def print_summary_to_console(self): # Renamed from print_summary
|
||
# (Implementation can be more detailed based on the new stats)
|
||
print("\n===== 测试运行摘要 =====")
|
||
print(f"开始时间: {self.start_time.isoformat()}")
|
||
if self.end_time:
|
||
print(f"结束时间: {self.end_time.isoformat()}")
|
||
print(f"总耗时: {self.duration:.2f} 秒")
|
||
|
||
print("\n--- 端点统计 ---")
|
||
print(f"定义的端点总数: {self.total_endpoints_defined}")
|
||
print(f"实际测试的端点数: {self.total_endpoints_tested}")
|
||
print(f" 通过: {self.endpoints_passed}")
|
||
print(f" 失败: {self.endpoints_failed}")
|
||
print(f" 部分成功: {self.endpoints_partial_success}")
|
||
print(f" 执行错误: {self.endpoints_error}")
|
||
print(f" 跳过执行: {self.endpoints_skipped}")
|
||
print(f" 端点通过率: {self.endpoint_success_rate:.2f}%")
|
||
|
||
print("\n--- 测试用例统计 ---")
|
||
print(f"适用的测试用例总数 (计划执行): {self.total_test_cases_applicable}")
|
||
print(f"实际执行的测试用例总数: {self.total_test_cases_executed}")
|
||
print(f" 通过: {self.test_cases_passed}")
|
||
print(f" 失败: {self.test_cases_failed}")
|
||
print(f" 执行错误 (测试用例代码问题): {self.test_cases_error}")
|
||
print(f" 跳过 (在端点内被跳过): {self.test_cases_skipped_in_endpoint}")
|
||
print(f" 测试用例通过率: {self.test_case_success_rate:.2f}%")
|
||
|
||
# 可选：打印失败的端点和测试用例摘要
|
||
failed_endpoints = [res for res in self.detailed_results if res.overall_status == TestResult.Status.FAILED]
|
||
if failed_endpoints:
|
||
print("\n--- 失败的端点摘要 ---")
|
||
for ep_res in failed_endpoints:
|
||
print(f" 端点: {ep_res.endpoint_id} ({ep_res.endpoint_name}) - 状态: {ep_res.overall_status.value}")
|
||
for tc_res in ep_res.executed_test_cases:
|
||
if tc_res.status == ExecutedTestCaseResult.Status.FAILED:
|
||
print(f" - 测试用例失败: {tc_res.test_case_id} ({tc_res.test_case_name})")
|
||
for vp in tc_res.validation_points:
|
||
if not vp.passed:
|
||
print(f" - 验证点: {vp.message}")
|
||
|
||
# 新增：打印场景测试摘要 -> 修改为 Stage 测试摘要
|
||
if self.total_stages_defined > 0 or self.total_stages_executed > 0:
|
||
print("\n--- API测试阶段 (Stage) 统计 ---")
|
||
print(f"定义的API阶段总数: {self.total_stages_defined}")
|
||
print(f"实际执行的API阶段数: {self.total_stages_executed}")
|
||
print(f" 通过: {self.stages_passed}")
|
||
print(f" 失败: {self.stages_failed}")
|
||
print(f" 跳过: {self.stages_skipped}")
|
||
if self.total_stages_executed > 0:
|
||
print(f" 阶段通过率: {(self.stages_passed / self.total_stages_executed * 100):.2f}%")
|
||
|
||
failed_stages = [res for res in self.detailed_stage_results if res.overall_status == ExecutedStageResult.Status.FAILED]
|
||
if failed_stages:
|
||
print("\n--- 失败的API阶段摘要 ---")
|
||
for st_res in failed_stages:
|
||
print(f" 阶段: {st_res.stage_id} ({st_res.stage_name}) - 应用于分组: '{st_res.api_group_metadata.get('name', 'N/A')}' - 状态: {st_res.overall_status.value}")
|
||
for step_res in st_res.executed_steps:
|
||
if step_res.status == ExecutedStageStepResult.Status.FAILED:
|
||
print(f" - 步骤失败: {step_res.step_name} - 消息: {step_res.message}")
|
||
elif step_res.status == ExecutedStageStepResult.Status.ERROR:
|
||
print(f" - 步骤错误: {step_res.step_name} - 消息: {step_res.message}")
|
||
|
||
class APITestOrchestrator:
|
||
"""
|
||
测试编排器，负责协调整个API测试流程。
|
||
包括：
|
||
1. 解析API定义 (YAPI, Swagger)
|
||
2. 加载自定义测试用例 (BaseAPITestCase)
|
||
3. 执行测试用例并收集结果
|
||
4. 加载和执行API场景 (BaseAPIScenario) - 已实现
|
||
5. 加载和执行API测试阶段 (BaseAPIStage) - 新增
|
||
6. 生成测试报告和API调用详情
|
||
"""
|
||
|
||
def __init__(self, base_url: str,
|
||
custom_test_cases_dir: Optional[str] = None,
|
||
scenarios_dir: Optional[str] = None, # Keep existing scenarios_dir
|
||
stages_dir: Optional[str] = None, # New: Directory for stages
|
||
llm_api_key: Optional[str] = None,
|
||
llm_base_url: Optional[str] = None,
|
||
llm_model_name: Optional[str] = None,
|
||
use_llm_for_request_body: bool = False,
|
||
use_llm_for_path_params: bool = False,
|
||
use_llm_for_query_params: bool = False,
|
||
use_llm_for_headers: bool = False,
|
||
output_dir: Optional[str] = None
|
||
):
|
||
self.base_url = base_url.rstrip('/')
|
||
self.logger = logging.getLogger(__name__)
|
||
self.parser = InputParser() # Initialize the parser
|
||
self.api_caller = APICaller() # APICaller does not take base_url in __init__
|
||
self.json_validator = JSONSchemaValidator()
|
||
|
||
self.test_case_registry: Optional[TestCaseRegistry] = None # Initialize as Optional
|
||
if custom_test_cases_dir:
|
||
self.logger.info(f"正在从目录加载自定义测试用例: {custom_test_cases_dir}")
|
||
try:
|
||
self.test_case_registry = TestCaseRegistry(test_cases_dir=custom_test_cases_dir)
|
||
self.logger.info(f"加载了 {len(self.test_case_registry.get_all_test_case_classes())} 个自定义测试用例。")
|
||
except Exception as e:
|
||
self.logger.error(f"初始化 TestCaseRegistry 或加载测试用例失败: {e}", exc_info=True)
|
||
self.test_case_registry = None #确保在出错时 registry 为 None
|
||
else:
|
||
self.logger.info("未提供自定义测试用例目录，跳过加载自定义测试用例。")
|
||
|
||
# Scenario Registry (existing)
|
||
self.scenario_registry: Optional[ScenarioRegistry] = None # Initialize as Optional
|
||
if scenarios_dir:
|
||
self.logger.info(f"正在从目录加载API场景: {scenarios_dir}")
|
||
self.scenario_registry = ScenarioRegistry()
|
||
self.scenario_registry.discover_and_load_scenarios(scenarios_dir)
|
||
self.logger.info(f"加载了 {len(self.scenario_registry.get_all_scenario_classes())} 个API场景。")
|
||
else:
|
||
self.logger.info("未提供API场景目录，跳过加载场景测试。")
|
||
|
||
# Stage Registry (New)
|
||
self.stage_registry: Optional[StageRegistry] = None
|
||
if stages_dir:
|
||
self.logger.info(f"APITestOrchestrator: 尝试从目录加载API测试阶段: {stages_dir}")
|
||
try:
|
||
self.stage_registry = StageRegistry(stages_dir=stages_dir) # Pass stages_dir to constructor
|
||
# Discovery now happens within StageRegistry.__init__
|
||
self.logger.info(f"APITestOrchestrator: StageRegistry 初始化完毕。加载的API测试阶段数量: {len(self.stage_registry.get_all_stage_classes())}")
|
||
load_errors = self.stage_registry.get_load_errors()
|
||
if load_errors:
|
||
for err in load_errors:
|
||
self.logger.error(f"APITestOrchestrator: StageRegistry 加载错误: {err}")
|
||
except Exception as e:
|
||
self.logger.error(f"APITestOrchestrator: 初始化 StageRegistry 时发生错误: {e}", exc_info=True)
|
||
self.stage_registry = None # 确保出错时为 None
|
||
else:
|
||
self.logger.info("APITestOrchestrator: 未提供API测试阶段目录，跳过加载阶段测试。")
|
||
|
||
# LLM Service Initialization
|
||
self.llm_service: Optional[LLMService] = None
|
||
if LLMService and llm_api_key:
|
||
try:
|
||
self.llm_service = LLMService(api_key=llm_api_key, base_url=llm_base_url, model_name=llm_model_name)
|
||
self.logger.info(f"LLMService initialized successfully with model: {llm_model_name or 'default'}.")
|
||
except Exception as e:
|
||
self.logger.error(f"LLMService initialization failed: {e}. LLM features will be disabled.", exc_info=True)
|
||
self.llm_service = None
|
||
elif LLMService and not llm_api_key:
|
||
self.logger.info("LLMService is available, but LLM API key was not provided. LLM features will be disabled.")
|
||
|
||
self.use_llm_for_request_body = use_llm_for_request_body
|
||
self.use_llm_for_path_params = use_llm_for_path_params
|
||
self.use_llm_for_query_params = use_llm_for_query_params
|
||
self.use_llm_for_headers = use_llm_for_headers
|
||
|
||
self.llm_endpoint_params_cache: Dict[str, Dict[str, Any]] = {}
|
||
|
||
if (self.use_llm_for_request_body or \
|
||
self.use_llm_for_path_params or \
|
||
self.use_llm_for_query_params or \
|
||
self.use_llm_for_headers) and not self.llm_service:
|
||
self.logger.warning("LLM-based data generation was enabled, but LLMService is not available or failed to initialize. Disabling all LLM features.")
|
||
self.use_llm_for_request_body = False
|
||
self.use_llm_for_path_params = False
|
||
self.use_llm_for_query_params = False
|
||
self.use_llm_for_headers = False
|
||
|
||
def get_api_call_details(self) -> List[APICallDetail]:
|
||
"""Returns the collected list of API call details."""
|
||
return self.api_call_details_log
|
||
|
||
def _should_use_llm_for_param_type(
|
||
self,
|
||
param_type_key: str, # 例如 "path_params", "query_params", "headers", "body"
|
||
test_case_instance: Optional[BaseAPITestCase]
|
||
) -> bool:
|
||
"""
|
||
判断是否应为特定参数类型尝试使用LLM。
|
||
结合全局配置和测试用例特定配置。
|
||
"""
|
||
if not self.llm_service: # 如果LLM服务本身就不可用，则肯定不用
|
||
return False
|
||
|
||
global_flag = False
|
||
tc_specific_flag: Optional[bool] = None
|
||
|
||
if param_type_key == "body":
|
||
global_flag = self.use_llm_for_request_body
|
||
if test_case_instance:
|
||
tc_specific_flag = test_case_instance.use_llm_for_body
|
||
elif param_type_key == "path_params":
|
||
global_flag = self.use_llm_for_path_params
|
||
if test_case_instance:
|
||
tc_specific_flag = test_case_instance.use_llm_for_path_params
|
||
elif param_type_key == "query_params":
|
||
global_flag = self.use_llm_for_query_params
|
||
if test_case_instance:
|
||
tc_specific_flag = test_case_instance.use_llm_for_query_params
|
||
elif param_type_key == "headers":
|
||
global_flag = self.use_llm_for_headers
|
||
if test_case_instance:
|
||
tc_specific_flag = test_case_instance.use_llm_for_headers
|
||
else:
|
||
self.logger.warning(f"未知的参数类型键 '{param_type_key}' 在 _should_use_llm_for_param_type 中检查。")
|
||
return False
|
||
|
||
# 决定最终是否使用LLM的逻辑：
|
||
# 1. 如果测试用例明确设置了 (tc_specific_flag is not None)，则以测试用例的设置为准。
|
||
# 2. 否则，使用全局设置。
|
||
final_decision = tc_specific_flag if tc_specific_flag is not None else global_flag
|
||
|
||
# self.logger.debug(f"LLM决策 for '{param_type_key}': TC specific='{tc_specific_flag}', Global='{global_flag}', Final='{final_decision}')
|
||
return final_decision
|
||
|
||
def _create_pydantic_model_from_schema(
|
||
self,
|
||
schema: Dict[str, Any],
|
||
model_name: str,
|
||
recursion_depth: int = 0
|
||
) -> Optional[Type[BaseModel]]:
|
||
"""
|
||
Dynamically creates a Pydantic model from a JSON schema.
|
||
Handles nested schemas, arrays, and various OpenAPI/JSON Schema constructs.
|
||
Uses a cache (_dynamic_model_cache) to avoid redefining identical models.
|
||
"""
|
||
# This cache key generation might need refinement for very complex/deep schemas
|
||
# For now, using a combination of model_name and sorted schema keys/values
|
||
# Important: dicts are unhashable, so we convert to a sorted tuple of items for the cache key.
|
||
# This is a simplified cache key; a more robust approach might involve serializing the schema.
|
||
# schema_tuple_for_key = tuple(sorted(schema.items())) if isinstance(schema, dict) else schema
|
||
# cache_key = (model_name, schema_tuple_for_key, recursion_depth) # Might be too verbose/complex
|
||
|
||
# Simpler cache key based on model_name only if we assume model_name is sufficiently unique
|
||
# for a given schema structure within a run. If schemas can change for the same model_name,
|
||
# this needs to be more sophisticated.
|
||
|
||
# If model_name is unique per structure, this is fine.
|
||
# Let's assume model_name is carefully constructed to be unique for each distinct schema structure
|
||
# by the calling functions (e.g., _generate_data_from_schema, _build_object_schema_for_params).
|
||
|
||
# Simplified approach: if a model with this exact name was already created, reuse it.
|
||
# This relies on the caller to ensure `model_name` is unique per schema structure.
|
||
if model_name in _dynamic_model_cache:
|
||
self.logger.debug(f"Reusing cached Pydantic model: {model_name}")
|
||
return _dynamic_model_cache[model_name]
|
||
|
||
if recursion_depth > self.MAX_RECURSION_DEPTH_PYDANTIC:
|
||
self.logger.error(f"创建Pydantic模型 '{model_name}' 时达到最大递归深度 {self.MAX_RECURSION_DEPTH_PYDANTIC}。可能存在循环引用。")
|
||
return None
|
||
|
||
# 清理模型名称，使其成为有效的Python标识符
|
||
safe_model_name = "".join(c if c.isalnum() or c == '_' else '_' for c in model_name)
|
||
if not safe_model_name or not safe_model_name[0].isalpha() and safe_model_name[0] != '_':
|
||
safe_model_name = f"DynamicModel_{safe_model_name}"
|
||
|
||
# 检查缓存 (使用清理后的名称)
|
||
if safe_model_name in _dynamic_model_cache:
|
||
self.logger.debug(f"从缓存返回动态模型: {safe_model_name}")
|
||
return _dynamic_model_cache[safe_model_name]
|
||
|
||
self.logger.debug(f"开始从Schema创建Pydantic模型: '{safe_model_name}' (原始名: '{model_name}', 深度: {recursion_depth})")
|
||
|
||
if not isinstance(schema, dict) or schema.get('type') != 'object':
|
||
# Safely get type for logging if schema is not a dict or does not have 'type'
|
||
schema_type_for_log = schema.get('type') if isinstance(schema, dict) else type(schema).__name__
|
||
self.logger.error(f"提供的Schema用于模型 '{safe_model_name}' 的必须是 type 'object' 且是一个字典, 实际: {schema_type_for_log}")
|
||
return None
|
||
|
||
properties = schema.get('properties', {})
|
||
required_fields = set(schema.get('required', []))
|
||
field_definitions: Dict[str, Tuple[Any, Any]] = {}
|
||
|
||
for prop_name, prop_schema in properties.items():
|
||
if not isinstance(prop_schema, dict):
|
||
self.logger.warning(f"属性 '{prop_name}' 在模型 '{safe_model_name}' 中的Schema无效，已跳过。")
|
||
continue
|
||
|
||
python_type: Any = Any
|
||
field_args: Dict[str, Any] = {}
|
||
|
||
default_value: Any = ... # Ellipsis for required fields with no default
|
||
if 'default' in prop_schema:
|
||
default_value = prop_schema['default']
|
||
elif prop_name not in required_fields:
|
||
default_value = None
|
||
|
||
if 'description' in prop_schema:
|
||
field_args['description'] = prop_schema['description']
|
||
|
||
json_type = prop_schema.get('type')
|
||
json_format = prop_schema.get('format')
|
||
|
||
if json_type == 'object':
|
||
nested_model_name_base = f"{safe_model_name}_{prop_name}"
|
||
python_type = self._create_pydantic_model_from_schema(prop_schema, nested_model_name_base, recursion_depth + 1)
|
||
if python_type is None:
|
||
self.logger.warning(f"无法为 '{safe_model_name}' 中的嵌套属性 '{prop_name}' 创建模型，已跳过。")
|
||
continue
|
||
elif json_type == 'array':
|
||
items_schema = prop_schema.get('items')
|
||
if not isinstance(items_schema, dict):
|
||
self.logger.warning(f"数组属性 '{prop_name}' 在模型 '{safe_model_name}' 中的 'items' schema无效，已跳过。")
|
||
continue
|
||
|
||
item_type: Any = Any
|
||
item_json_type = items_schema.get('type')
|
||
item_json_format = items_schema.get('format')
|
||
|
||
if item_json_type == 'object':
|
||
item_model_name_base = f"{safe_model_name}_{prop_name}_Item"
|
||
item_type = self._create_pydantic_model_from_schema(items_schema, item_model_name_base, recursion_depth + 1)
|
||
if item_type is None:
|
||
self.logger.warning(f"无法为 '{safe_model_name}' 中的数组属性 '{prop_name}' 的项创建模型，已跳过。")
|
||
continue
|
||
elif item_json_type == 'string':
|
||
if item_json_format == 'date-time': item_type = dt.datetime
|
||
elif item_json_format == 'date': item_type = dt.date
|
||
elif item_json_format == 'email': item_type = EmailStr
|
||
elif item_json_format == 'uuid': item_type = UUID
|
||
else: item_type = str
|
||
elif item_json_type == 'integer': item_type = int
|
||
elif item_json_type == 'number': item_type = float
|
||
elif item_json_type == 'boolean': item_type = bool
|
||
else:
|
||
self.logger.warning(f"数组 '{prop_name}' 中的项具有未知类型 '{item_json_type}'，默认为 Any。")
|
||
|
||
python_type = List[item_type] # type: ignore
|
||
elif json_type == 'string':
|
||
if json_format == 'date-time': python_type = dt.datetime
|
||
elif json_format == 'date': python_type = dt.date
|
||
elif json_format == 'email': python_type = EmailStr
|
||
elif json_format == 'uuid': python_type = UUID
|
||
else: python_type = str
|
||
if 'minLength' in prop_schema: field_args['min_length'] = prop_schema['minLength']
|
||
if 'maxLength' in prop_schema: field_args['max_length'] = prop_schema['maxLength']
|
||
if 'pattern' in prop_schema: field_args['pattern'] = prop_schema['pattern']
|
||
elif json_type == 'integer':
|
||
python_type = int
|
||
if 'minimum' in prop_schema: field_args['ge'] = prop_schema['minimum']
|
||
if 'maximum' in prop_schema: field_args['le'] = prop_schema['maximum']
|
||
elif json_type == 'number':
|
||
python_type = float
|
||
if 'minimum' in prop_schema: field_args['ge'] = prop_schema['minimum']
|
||
if 'maximum' in prop_schema: field_args['le'] = prop_schema['maximum']
|
||
elif json_type == 'boolean':
|
||
python_type = bool
|
||
elif json_type is None and '$ref' in prop_schema:
|
||
self.logger.warning(f"Schema $ref '{prop_schema['$ref']}' in '{safe_model_name}.{prop_name}' not yet supported. Defaulting to Any.")
|
||
python_type = Any
|
||
else:
|
||
self.logger.warning(f"属性 '{prop_name}' 在模型 '{safe_model_name}' 中具有未知类型 '{json_type}'，默认为 Any。")
|
||
python_type = Any
|
||
|
||
if 'enum' in prop_schema:
|
||
enum_values = prop_schema['enum']
|
||
if enum_values:
|
||
enum_desc = f" (Enum values: {', '.join(map(str, enum_values))})"
|
||
field_args['description'] = field_args.get('description', '') + enum_desc
|
||
|
||
current_field_is_optional = prop_name not in required_fields
|
||
if current_field_is_optional and python_type is not Any and default_value is None:
|
||
# For Pydantic v1/v2, if a field is not required and has no other default, it's Optional.
|
||
# The `python_type` itself might already be an `Optional` if it came from a nested optional model.
|
||
# We only wrap with Optional if it's not already wrapped effectively.
|
||
# A simple check: if the type name doesn't start with "Optional"
|
||
if not (hasattr(python_type, '__origin__') and python_type.__origin__ is Union and type(None) in python_type.__args__):
|
||
python_type = Optional[python_type]
|
||
|
||
|
||
field_definitions[prop_name] = (python_type, Field(default_value, **field_args))
|
||
|
||
if not field_definitions:
|
||
self.logger.warning(f"模型 '{safe_model_name}' 没有有效的字段定义，无法创建。")
|
||
# Return a very basic BaseModel if no properties are defined but an object schema was given
|
||
# This might happen for an empty object schema {}
|
||
try:
|
||
EmptyModel = create_model(safe_model_name, __base__=BaseModel)
|
||
_dynamic_model_cache[safe_model_name] = EmptyModel
|
||
self.logger.info(f"创建了一个空的动态Pydantic模型: '{safe_model_name}' (由于无属性定义)")
|
||
return EmptyModel
|
||
except Exception as e_empty:
|
||
self.logger.error(f"尝试为 '{safe_model_name}' 创建空模型时失败: {e_empty}", exc_info=True)
|
||
return None
|
||
|
||
|
||
try:
|
||
# ForwardRef for self-referencing models is complex; not fully handled here yet.
|
||
# If a type in field_definitions is a string (e.g., a ForwardRef string), create_model handles it.
|
||
DynamicModel = create_model(safe_model_name, **field_definitions, __base__=BaseModel) # type: ignore
|
||
_dynamic_model_cache[safe_model_name] = DynamicModel
|
||
self.logger.info(f"成功创建/缓存了动态Pydantic模型: '{safe_model_name}'")
|
||
|
||
# Attempt to update forward refs if any were string types that are now defined
|
||
# This is a simplified approach. Pydantic's update_forward_refs is usually called on the module or specific model.
|
||
# For dynamically created models, this might need careful handling if true circular deps are common.
|
||
# For now, we assume nested creation order mostly handles dependencies.
|
||
# if hasattr(DynamicModel, 'update_forward_refs'):
|
||
# try:
|
||
# DynamicModel.update_forward_refs(**_dynamic_model_cache)
|
||
# self.logger.debug(f"Attempted to update forward refs for {safe_model_name}")
|
||
# except Exception as e_fwd:
|
||
# self.logger.warning(f"Error updating forward_refs for {safe_model_name}: {e_fwd}")
|
||
|
||
return DynamicModel
|
||
except Exception as e:
|
||
self.logger.error(f"使用Pydantic create_model创建 '{safe_model_name}' 时失败: {e}", exc_info=True)
|
||
return None
|
||
|
||
def _execute_single_test_case(
|
||
self,
|
||
test_case_class: Type[BaseAPITestCase],
|
||
endpoint_spec: Union[YAPIEndpoint, SwaggerEndpoint], # 当前端点的规格
|
||
global_api_spec: Union[ParsedYAPISpec, ParsedSwaggerSpec] # 整个API的规格
|
||
) -> ExecutedTestCaseResult:
|
||
"""
|
||
执行单个测试用例。
|
||
|
||
流程:
|
||
1. 准备请求数据 (路径参数, 查询参数, 请求头, 请求体)。
|
||
- 首先尝试从测试用例的 generate_xxx 方法获取。
|
||
- 如果测试用例未覆盖或返回None，则尝试从API spec生成默认数据。
|
||
- 如果开启了LLM，并且测试用例允许，则使用LLM生成。
|
||
2. (如果适用) 调用测试用例的 modify_request_url 钩子。
|
||
3. (如果适用) 调用测试用例的 validate_request_url, validate_request_headers, validate_request_body 钩子。
|
||
4. 发送API请求。
|
||
5. 记录响应。
|
||
6. 调用测试用例的 validate_response 和 check_performance 钩子。
|
||
7. 汇总验证结果，确定测试用例状态。
|
||
"""
|
||
start_time = time.monotonic()
|
||
validation_results: List[ValidationResult] = []
|
||
overall_status: ExecutedTestCaseResult.Status
|
||
execution_message = ""
|
||
test_case_instance: Optional[BaseAPITestCase] = None # Initialize to None
|
||
|
||
# 将 endpoint_spec 转换为字典，如果它还不是的话
|
||
endpoint_spec_dict: Dict[str, Any]
|
||
if isinstance(endpoint_spec, dict):
|
||
endpoint_spec_dict = endpoint_spec
|
||
# self.logger.debug(f"endpoint_spec 已经是字典类型。")
|
||
elif hasattr(endpoint_spec, 'to_dict') and callable(endpoint_spec.to_dict):
|
||
try:
|
||
endpoint_spec_dict = endpoint_spec.to_dict()
|
||
# self.logger.debug(f"成功通过 to_dict() 方法将类型为 {type(endpoint_spec)} 的 endpoint_spec 转换为字典。")
|
||
if not endpoint_spec_dict: # 如果 to_dict() 返回空字典
|
||
# self.logger.warning(f"endpoint_spec.to_dict() (类型: {type(endpoint_spec)}) 返回了一个空字典。")
|
||
# 尝试备用转换
|
||
if isinstance(endpoint_spec, (YAPIEndpoint, SwaggerEndpoint)):
|
||
# self.logger.debug(f"尝试从 {type(endpoint_spec).__name__} 对象的属性手动构建 endpoint_spec_dict。")
|
||
endpoint_spec_dict = {
|
||
"method": getattr(endpoint_spec, 'method', 'UNKNOWN_METHOD').upper(),
|
||
"path": getattr(endpoint_spec, 'path', 'UNKNOWN_PATH'),
|
||
"title": getattr(endpoint_spec, 'title', getattr(endpoint_spec, 'summary', '')),
|
||
"summary": getattr(endpoint_spec, 'summary', ''),
|
||
"description": getattr(endpoint_spec, 'description', ''),
|
||
"operationId": getattr(endpoint_spec, 'operation_id', f"{getattr(endpoint_spec, 'method', '').upper()}_{getattr(endpoint_spec, 'path', '').replace('/', '_')}"),
|
||
"parameters": getattr(endpoint_spec, 'parameters', []) if hasattr(endpoint_spec, 'parameters') else (getattr(endpoint_spec, 'req_query', []) + getattr(endpoint_spec, 'req_headers', [])),
|
||
"requestBody": getattr(endpoint_spec, 'request_body', None) if hasattr(endpoint_spec, 'request_body') else getattr(endpoint_spec, 'req_body_other', None),
|
||
"_original_object_type": type(endpoint_spec).__name__
|
||
}
|
||
if not any(endpoint_spec_dict.values()): # 如果手动构建后仍基本为空
|
||
# self.logger.error(f"手动从属性构建 endpoint_spec_dict (类型: {type(endpoint_spec)}) 后仍然为空或无效。")
|
||
endpoint_spec_dict = {} # 重置为空，触发下方错误处理
|
||
except Exception as e:
|
||
self.logger.error(f"调用 endpoint_spec (类型: {type(endpoint_spec)}) 的 to_dict() 方法时出错: {e}。尝试备用转换。")
|
||
if isinstance(endpoint_spec, (YAPIEndpoint, SwaggerEndpoint)):
|
||
self.logger.debug(f"尝试从 {type(endpoint_spec).__name__} 对象的属性手动构建 endpoint_spec_dict。")
|
||
endpoint_spec_dict = {
|
||
"method": getattr(endpoint_spec, 'method', 'UNKNOWN_METHOD').upper(),
|
||
"path": getattr(endpoint_spec, 'path', 'UNKNOWN_PATH'),
|
||
"title": getattr(endpoint_spec, 'title', getattr(endpoint_spec, 'summary', '')),
|
||
"summary": getattr(endpoint_spec, 'summary', ''),
|
||
"description": getattr(endpoint_spec, 'description', ''),
|
||
"operationId": getattr(endpoint_spec, 'operation_id', f"{getattr(endpoint_spec, 'method', '').upper()}_{getattr(endpoint_spec, 'path', '').replace('/', '_')}"),
|
||
"parameters": getattr(endpoint_spec, 'parameters', []) if hasattr(endpoint_spec, 'parameters') else (getattr(endpoint_spec, 'req_query', []) + getattr(endpoint_spec, 'req_headers', [])),
|
||
"requestBody": getattr(endpoint_spec, 'request_body', None) if hasattr(endpoint_spec, 'request_body') else getattr(endpoint_spec, 'req_body_other', None),
|
||
"_original_object_type": type(endpoint_spec).__name__
|
||
}
|
||
if not any(endpoint_spec_dict.values()): # 如果手动构建后仍基本为空
|
||
self.logger.error(f"手动从属性构建 endpoint_spec_dict (类型: {type(endpoint_spec)}) 后仍然为空或无效。")
|
||
endpoint_spec_dict = {} # 重置为空，触发下方错误处理
|
||
else:
|
||
endpoint_spec_dict = {} # 转换失败
|
||
elif hasattr(endpoint_spec, 'data') and isinstance(getattr(endpoint_spec, 'data'), dict): # 兼容 YAPIEndpoint 结构
|
||
endpoint_spec_dict = getattr(endpoint_spec, 'data')
|
||
# self.logger.debug(f"使用了类型为 {type(endpoint_spec)} 的 endpoint_spec 的 .data 属性。")
|
||
else: # 如果没有 to_dict, 也不是已知可直接访问 .data 的类型，则尝试最后的通用转换或手动构建
|
||
if isinstance(endpoint_spec, (YAPIEndpoint, SwaggerEndpoint)):
|
||
# self.logger.debug(f"类型为 {type(endpoint_spec).__name__} 的 endpoint_spec 没有 to_dict() 或 data，尝试从属性手动构建。")
|
||
endpoint_spec_dict = {
|
||
"method": getattr(endpoint_spec, 'method', 'UNKNOWN_METHOD').upper(),
|
||
"path": getattr(endpoint_spec, 'path', 'UNKNOWN_PATH'),
|
||
"title": getattr(endpoint_spec, 'title', getattr(endpoint_spec, 'summary', '')),
|
||
"summary": getattr(endpoint_spec, 'summary', ''),
|
||
"description": getattr(endpoint_spec, 'description', ''),
|
||
"operationId": getattr(endpoint_spec, 'operation_id', f"{getattr(endpoint_spec, 'method', '').upper()}_{getattr(endpoint_spec, 'path', '').replace('/', '_')}"),
|
||
"parameters": getattr(endpoint_spec, 'parameters', []) if hasattr(endpoint_spec, 'parameters') else (getattr(endpoint_spec, 'req_query', []) + getattr(endpoint_spec, 'req_headers', [])),
|
||
"requestBody": getattr(endpoint_spec, 'request_body', None) if hasattr(endpoint_spec, 'request_body') else getattr(endpoint_spec, 'req_body_other', None),
|
||
"_original_object_type": type(endpoint_spec).__name__
|
||
}
|
||
if not any(endpoint_spec_dict.values()): # 如果手动构建后仍基本为空
|
||
self.logger.error(f"手动从属性构建 endpoint_spec_dict (类型: {type(endpoint_spec)}) 后仍然为空或无效。")
|
||
endpoint_spec_dict = {} # 重置为空，触发下方错误处理
|
||
else:
|
||
try:
|
||
endpoint_spec_dict = dict(endpoint_spec)
|
||
self.logger.warning(f"直接将类型为 {type(endpoint_spec)} 的 endpoint_spec 转换为字典。这可能是一个浅拷贝，并且可能不完整。")
|
||
except TypeError:
|
||
self.logger.error(f"无法将 endpoint_spec (类型: {type(endpoint_spec)}) 转换为字典，也未找到有效的转换方法。")
|
||
endpoint_spec_dict = {}
|
||
|
||
if not endpoint_spec_dict or not endpoint_spec_dict.get("path") or endpoint_spec_dict.get("path") == 'UNKNOWN_PATH': # 如果转换后仍为空或无效
|
||
self.logger.error(f"Endpoint spec (原始类型: {type(endpoint_spec)}) 无法有效转换为包含有效路径的字典，测试用例执行可能受影响。最终 endpoint_spec_dict: {endpoint_spec_dict}")
|
||
# 创建一个最小的 endpoint_spec_dict 以允许测试用例实例化，但它将缺少大部分信息
|
||
endpoint_spec_dict = {
|
||
'method': endpoint_spec_dict.get('method', 'UNKNOWN_METHOD'), # 保留已解析的方法
|
||
'path': 'UNKNOWN_PATH_CONVERSION_FAILED',
|
||
'title': f"Unknown endpoint due to spec conversion error for original type {type(endpoint_spec)}",
|
||
'parameters': [], # 确保有空的 parameters 和 requestBody
|
||
'requestBody': None
|
||
}
|
||
|
||
# 确保 global_api_spec (应该是 ParsedSwaggerSpec 或 ParsedYAPISpec 实例) 被转换为字典
|
||
global_spec_dict: Dict[str, Any] = {}
|
||
converted_by_method: Optional[str] = None
|
||
|
||
if hasattr(global_api_spec, 'spec') and isinstance(getattr(global_api_spec, 'spec', None), dict) and getattr(global_api_spec, 'spec', None):
|
||
global_spec_dict = global_api_spec.spec # type: ignore
|
||
converted_by_method = ".spec attribute"
|
||
elif is_dataclass(global_api_spec) and not isinstance(global_api_spec, type): # Ensure it's an instance, not the class itself
|
||
try:
|
||
candidate_spec = dataclass_asdict(global_api_spec)
|
||
if isinstance(candidate_spec, dict) and candidate_spec:
|
||
global_spec_dict = candidate_spec
|
||
converted_by_method = "dataclasses.asdict()"
|
||
except Exception as e:
|
||
self.logger.debug(f"Calling dataclasses.asdict() on {type(global_api_spec)} failed: {e}, trying other methods.")
|
||
|
||
if not global_spec_dict and hasattr(global_api_spec, 'model_dump') and callable(global_api_spec.model_dump):
|
||
try:
|
||
candidate_spec = global_api_spec.model_dump()
|
||
if isinstance(candidate_spec, dict) and candidate_spec:
|
||
global_spec_dict = candidate_spec
|
||
converted_by_method = ".model_dump()"
|
||
except Exception as e:
|
||
self.logger.debug(f"Calling .model_dump() on {type(global_api_spec)} failed: {e}, trying other methods.")
|
||
|
||
if not global_spec_dict and hasattr(global_api_spec, 'dict') and callable(global_api_spec.dict):
|
||
try:
|
||
candidate_spec = global_api_spec.dict()
|
||
if isinstance(candidate_spec, dict) and candidate_spec:
|
||
global_spec_dict = candidate_spec
|
||
converted_by_method = ".dict()"
|
||
except Exception as e:
|
||
self.logger.debug(f"Calling .dict() on {type(global_api_spec)} failed: {e}, trying other methods.")
|
||
|
||
if not global_spec_dict and hasattr(global_api_spec, 'to_dict') and callable(global_api_spec.to_dict):
|
||
try:
|
||
candidate_spec = global_api_spec.to_dict()
|
||
if isinstance(candidate_spec, dict) and candidate_spec:
|
||
global_spec_dict = candidate_spec
|
||
converted_by_method = ".to_dict()"
|
||
except Exception as e:
|
||
self.logger.debug(f"Calling .to_dict() on {type(global_api_spec)} failed: {e}, trying other methods.")
|
||
|
||
if not global_spec_dict and isinstance(global_api_spec, dict) and global_api_spec:
|
||
global_spec_dict = global_api_spec
|
||
converted_by_method = "direct dict"
|
||
self.logger.warning(f"global_api_spec was already a dictionary. This might be unexpected if an object was anticipated.")
|
||
|
||
if global_spec_dict and converted_by_method:
|
||
self.logger.debug(f"Successfully converted/retrieved global_api_spec (type: {type(global_api_spec)}) to dict using {converted_by_method}.")
|
||
elif not global_spec_dict :
|
||
self.logger.error(
|
||
f"Failed to convert global_api_spec (type: {type(global_api_spec)}) to a non-empty dictionary using .spec, dataclasses.asdict(), .model_dump(), .dict(), or .to_dict(). "
|
||
f"It's also not a non-empty dictionary itself. JSON reference resolution will be severely limited or fail. Using empty global_spec_dict."
|
||
)
|
||
global_spec_dict = {}
|
||
|
||
# --- BEGIN $ref RESOLUTION ---
|
||
if global_spec_dict: # Only attempt resolution if we have the full spec for lookups
|
||
self.logger.debug(f"global_spec_dict keys for $ref resolution: {list(global_spec_dict.keys())}") # <--- 添加的日志行
|
||
self.logger.debug(f"开始为 endpoint_spec_dict (来自 {type(endpoint_spec)}) 中的 schemas 进行 $ref 解析...")
|
||
|
||
# 1. 解析 requestBody schema
|
||
if 'requestBody' in endpoint_spec_dict and isinstance(endpoint_spec_dict['requestBody'], dict):
|
||
if 'content' in endpoint_spec_dict['requestBody'] and isinstance(endpoint_spec_dict['requestBody']['content'], dict):
|
||
for media_type, media_type_obj in endpoint_spec_dict['requestBody']['content'].items():
|
||
if isinstance(media_type_obj, dict) and 'schema' in media_type_obj:
|
||
self.logger.debug(f"正在解析 requestBody content '{media_type}' 的 schema...")
|
||
original_schema = media_type_obj['schema']
|
||
media_type_obj['schema'] = schema_utils.resolve_json_schema_references(original_schema, global_spec_dict)
|
||
# self.logger.debug(f"解析后的 requestBody content '{media_type}' schema: {json.dumps(media_type_obj['schema'], indent=2)}")
|
||
|
||
|
||
# 2. 解析 parameters schemas (OpenAPI 2.0 'in: body' parameter or OpenAPI 3.0 parameters)
|
||
if 'parameters' in endpoint_spec_dict and isinstance(endpoint_spec_dict['parameters'], list):
|
||
for i, param in enumerate(endpoint_spec_dict['parameters']):
|
||
if isinstance(param, dict) and 'schema' in param:
|
||
self.logger.debug(f"正在解析 parameters[{i}] ('{param.get('name', 'N/A')}') 的 schema...")
|
||
original_param_schema = param['schema']
|
||
param['schema'] = schema_utils.resolve_json_schema_references(original_param_schema, global_spec_dict)
|
||
# self.logger.debug(f"解析后的 parameters[{i}] schema: {json.dumps(param['schema'], indent=2)}")
|
||
|
||
# 3. 解析 responses schemas
|
||
if 'responses' in endpoint_spec_dict and isinstance(endpoint_spec_dict['responses'], dict):
|
||
for status_code, response_obj in endpoint_spec_dict['responses'].items():
|
||
if isinstance(response_obj, dict) and 'content' in response_obj and isinstance(response_obj['content'], dict):
|
||
for media_type, media_type_obj in response_obj['content'].items():
|
||
if isinstance(media_type_obj, dict) and 'schema' in media_type_obj:
|
||
self.logger.debug(f"正在解析 responses '{status_code}' content '{media_type}' 的 schema...")
|
||
original_resp_schema = media_type_obj['schema']
|
||
media_type_obj['schema'] = schema_utils.resolve_json_schema_references(original_resp_schema, global_spec_dict)
|
||
# self.logger.debug(f"解析后的 response '{status_code}' content '{media_type}' schema: {json.dumps(media_type_obj['schema'], indent=2)}")
|
||
# OpenAPI 2.0 response schema directly under response object
|
||
elif isinstance(response_obj, dict) and 'schema' in response_obj:
|
||
self.logger.debug(f"正在解析 responses '{status_code}' 的 schema (OpenAPI 2.0 style)...")
|
||
original_resp_schema = response_obj['schema']
|
||
response_obj['schema'] = schema_utils.resolve_json_schema_references(original_resp_schema, global_spec_dict)
|
||
|
||
|
||
self.logger.info(f"Endpoint spec (来自 {type(endpoint_spec)}) 中的 schemas $ref 解析完成。")
|
||
else:
|
||
self.logger.warning(f"global_spec_dict 为空，跳过 endpoint_spec_dict (来自 {type(endpoint_spec)}) 的 $ref 解析。")
|
||
# --- END $ref RESOLUTION ---
|
||
|
||
# 将 global_spec_dict 注入到 endpoint_spec_dict 中，供可能的内部解析使用 (如果 to_dict 未包含它)
|
||
if '_global_api_spec_for_resolution' not in endpoint_spec_dict and global_spec_dict:
|
||
endpoint_spec_dict['_global_api_spec_for_resolution'] = global_spec_dict
|
||
|
||
|
||
try:
|
||
self.logger.debug(f"准备实例化测试用例类: {test_case_class.__name__} 使用 endpoint_spec (keys: {list(endpoint_spec_dict.keys()) if endpoint_spec_dict else 'None'}) 和 global_api_spec (keys: {list(global_spec_dict.keys()) if global_spec_dict else 'None'})")
|
||
test_case_instance = test_case_class(
|
||
endpoint_spec=endpoint_spec_dict,
|
||
global_api_spec=global_spec_dict,
|
||
json_schema_validator=self.json_validator,
|
||
llm_service=self.llm_service # Pass the orchestrator's LLM service instance
|
||
)
|
||
self.logger.info(f"开始执行测试用例 '{test_case_instance.id}' ({test_case_instance.name}) for endpoint '{endpoint_spec_dict.get('method', 'N/A')} {endpoint_spec_dict.get('path', 'N/A')}'")
|
||
|
||
# 调用 _prepare_initial_request_data 时传递 test_case_instance
|
||
# 并直接解包返回的元组
|
||
request_context_data = self._prepare_initial_request_data(endpoint_spec_dict, test_case_instance=test_case_instance)
|
||
|
||
# 从 request_context_data 对象中获取各个部分
|
||
method = request_context_data.method
|
||
path_params_data = request_context_data.path_params
|
||
query_params_data = request_context_data.query_params
|
||
headers_data = request_context_data.headers
|
||
body_data = request_context_data.body
|
||
|
||
# 让测试用例有机会修改这些生成的数据
|
||
# 注意: BaseAPITestCase 中的 generate_* 方法现在需要传入 endpoint_spec_dict
|
||
# 因为它们可能需要原始的端点定义来进行更复杂的逻辑
|
||
current_q_params = test_case_instance.generate_query_params(query_params_data)
|
||
current_headers = test_case_instance.generate_headers(headers_data)
|
||
current_body = test_case_instance.generate_request_body(body_data)
|
||
# 路径参数通常由编排器根据路径模板和数据最终确定，但如果测试用例要覆盖，可以提供 generate_path_params
|
||
# 这里我们使用从 _prepare_initial_request_data 返回的 path_params_data 作为基础
|
||
current_path_params = test_case_instance.generate_path_params(path_params_data) if hasattr(test_case_instance, 'generate_path_params') and callable(getattr(test_case_instance, 'generate_path_params')) and getattr(test_case_instance, 'generate_path_params').__func__ != BaseAPITestCase.generate_path_params else path_params_data
|
||
|
||
|
||
final_url_template = endpoint_spec_dict.get('path', '')
|
||
# 添加日志：打印将要用于替换的路径参数
|
||
self.logger.debug(f"Path parameters to be substituted: {current_path_params}")
|
||
final_url = self.base_url + final_url_template
|
||
for p_name, p_val in current_path_params.items():
|
||
placeholder = f"{{{p_name}}}"
|
||
if placeholder in final_url_template: # 检查原始模板中是否存在占位符
|
||
final_url = final_url.replace(placeholder, str(p_val))
|
||
# 添加日志：打印替换后的URL (在测试用例修改之前)
|
||
self.logger.debug(f"URL after path parameter substitution (before TC modify_request_url hook): {final_url}")
|
||
|
||
# ---- 调用测试用例的 URL 修改钩子 ----
|
||
effective_url = final_url # 默认使用原始构建的URL
|
||
if hasattr(test_case_instance, 'modify_request_url') and callable(getattr(test_case_instance, 'modify_request_url')):
|
||
try:
|
||
modified_url_by_tc = test_case_instance.modify_request_url(final_url)
|
||
if modified_url_by_tc != final_url:
|
||
test_case_instance.logger.info(f"Test case '{test_case_instance.id}' modified URL from '{final_url}' to '{modified_url_by_tc}'")
|
||
effective_url = modified_url_by_tc # 使用测试用例修改后的URL
|
||
else:
|
||
test_case_instance.logger.debug(f"Test case '{test_case_instance.id}' did not modify the URL via modify_request_url hook.")
|
||
except Exception as e_url_mod:
|
||
test_case_instance.logger.error(f"Error in test case '{test_case_instance.id}' during modify_request_url: {e_url_mod}. Using original URL '{final_url}'.", exc_info=True)
|
||
# effective_url 保持为 final_url
|
||
else:
|
||
test_case_instance.logger.debug(f"Test case '{test_case_instance.id}' does not have a callable modify_request_url method. Using original URL.")
|
||
# ---- 结束 URL 修改钩子调用 ----
|
||
|
||
api_request_context = APIRequestContext(
|
||
method=method, # 使用从 _prepare_initial_request_data 获取的 method
|
||
url=effective_url, # <--- 使用 effective_url
|
||
path_params=current_path_params,
|
||
query_params=current_q_params,
|
||
headers=current_headers,
|
||
body=current_body,
|
||
endpoint_spec=endpoint_spec_dict
|
||
)
|
||
|
||
validation_results.extend(test_case_instance.validate_request_url(api_request_context.url, api_request_context))
|
||
validation_results.extend(test_case_instance.validate_request_headers(api_request_context.headers, api_request_context))
|
||
validation_results.extend(test_case_instance.validate_request_body(api_request_context.body, api_request_context))
|
||
|
||
critical_pre_validation_failure = False
|
||
failure_messages = []
|
||
for vp in validation_results:
|
||
if not vp.passed and test_case_instance.severity in [TestSeverity.CRITICAL, TestSeverity.HIGH]: # Check severity of the Test Case for pre-validation
|
||
critical_pre_validation_failure = True
|
||
failure_messages.append(vp.message)
|
||
|
||
if critical_pre_validation_failure:
|
||
self.logger.warning(f"测试用例 '{test_case_instance.id}' 因请求预校验失败而中止 (TC严重级别: {test_case_instance.severity.value})。失败信息: {'; '.join(failure_messages)}")
|
||
tc_duration = time.monotonic() - start_time
|
||
return ExecutedTestCaseResult(
|
||
test_case_id=test_case_instance.id,
|
||
test_case_name=test_case_instance.name,
|
||
test_case_severity=test_case_instance.severity,
|
||
status=ExecutedTestCaseResult.Status.FAILED,
|
||
validation_points=validation_results,
|
||
message=f"请求预校验失败: {'; '.join(failure_messages)}",
|
||
duration=tc_duration
|
||
)
|
||
|
||
api_request_obj = APIRequest(
|
||
method=api_request_context.method,
|
||
url=api_request_context.url,
|
||
params=api_request_context.query_params,
|
||
headers=api_request_context.headers,
|
||
json_data=api_request_context.body
|
||
)
|
||
|
||
response_call_start_time = time.time()
|
||
# api_response_obj = self.api_caller.call_api(api_request_obj)
|
||
api_response, api_call_detail = self.api_caller.call_api(api_request_obj)
|
||
self.api_call_details_log.append(api_call_detail) # 记录日志
|
||
|
||
response_call_elapsed_time = time.time() - response_call_start_time
|
||
|
||
actual_text_content: Optional[str] = None
|
||
# 使用解包后的 api_response:
|
||
if hasattr(api_response, 'text_content') and api_response.text_content is not None:
|
||
actual_text_content = api_response.text_content
|
||
elif api_response.json_content is not None: # <--- 使用 api_response
|
||
if isinstance(api_response.json_content, str):
|
||
actual_text_content = api_response.json_content
|
||
else:
|
||
try:
|
||
actual_text_content = json.dumps(api_response.json_content, ensure_ascii=False)
|
||
except TypeError:
|
||
actual_text_content = str(api_response.json_content)
|
||
|
||
api_response_context = APIResponseContext(
|
||
status_code=api_response.status_code, # <--- 使用 api_response
|
||
headers=api_response.headers, # <--- 使用 api_response
|
||
json_content=api_response.json_content, # <--- 使用 api_response
|
||
text_content=actual_text_content,
|
||
elapsed_time=response_call_elapsed_time,
|
||
original_response= getattr(api_response, 'raw_response', None), # <--- 使用 api_response
|
||
request_context=api_request_context
|
||
)
|
||
validation_results.extend(test_case_instance.validate_response(api_response_context, api_request_context))
|
||
validation_results.extend(test_case_instance.check_performance(api_response_context, api_request_context))
|
||
|
||
final_status = ExecutedTestCaseResult.Status.PASSED
|
||
if any(not vp.passed for vp in validation_results):
|
||
final_status = ExecutedTestCaseResult.Status.FAILED
|
||
|
||
tc_duration = time.monotonic() - start_time
|
||
return ExecutedTestCaseResult(
|
||
test_case_id=test_case_instance.id,
|
||
test_case_name=test_case_instance.name,
|
||
test_case_severity=test_case_instance.severity,
|
||
status=final_status,
|
||
validation_points=validation_results,
|
||
duration=tc_duration
|
||
)
|
||
|
||
except Exception as e:
|
||
self.logger.error(f"执行测试用例 '{test_case_class.id if hasattr(test_case_class, 'id') else test_case_class.__name__}' (在实例化阶段或之前) 时发生严重错误: {e}", exc_info=True)
|
||
# 如果 test_case_instance 在实例化时失败，它将是 None
|
||
tc_id_for_log = test_case_instance.id if test_case_instance else (test_case_class.id if hasattr(test_case_class, 'id') else "unknown_tc_id_instantiation_error")
|
||
tc_name_for_log = test_case_instance.name if test_case_instance else (test_case_class.name if hasattr(test_case_class, 'name') else test_case_class.__name__)
|
||
# 实例化失败，严重性默认为CRITICAL
|
||
tc_severity_for_log = test_case_instance.severity if test_case_instance else TestSeverity.CRITICAL
|
||
|
||
tc_duration = time.monotonic() - start_time
|
||
# validation_results 可能在此阶段为空，或包含来自先前步骤的条目（如果错误发生在实例化之后）
|
||
return ExecutedTestCaseResult(
|
||
test_case_id=tc_id_for_log,
|
||
test_case_name=tc_name_for_log,
|
||
test_case_severity=tc_severity_for_log,
|
||
status=ExecutedTestCaseResult.Status.ERROR,
|
||
validation_points=validation_results, # Ensure validation_results is defined (it is, at the start of the function)
|
||
message=f"测试用例执行时发生内部错误 (可能在实例化期间): {str(e)}",
|
||
duration=tc_duration
|
||
)
|
||
|
||
def _prepare_initial_request_data(
|
||
self,
|
||
endpoint_spec: Dict[str, Any], # 已经转换为字典
|
||
test_case_instance: Optional[BaseAPITestCase] = None # 传入测试用例实例以便访问其LLM配置
|
||
) -> APIRequestContext: # 返回 APIRequestContext 对象
|
||
"""
|
||
根据API端点规范，准备初始的请求数据，包括URL（模板）、路径参数、查询参数、头部和请求体。
|
||
这些数据将作为测试用例中 generate_* 方法的输入。
|
||
"""
|
||
method = endpoint_spec.get("method", "GET").upper()
|
||
path_template = endpoint_spec.get("path", "/")
|
||
operation_id = endpoint_spec.get("operationId", path_template) # 使用 path 作为 operationId 的 fallback
|
||
|
||
initial_path_params: Dict[str, Any] = {}
|
||
initial_query_params: Dict[str, Any] = {}
|
||
initial_headers: Dict[str, str] = {}
|
||
initial_body: Optional[Any] = None
|
||
|
||
parameters = endpoint_spec.get('parameters', [])
|
||
|
||
# 1. 处理路径参数
|
||
path_param_specs = [p for p in parameters if p.get('in') == 'path']
|
||
if path_param_specs:
|
||
should_use_llm = self._should_use_llm_for_param_type("path_params", test_case_instance)
|
||
if should_use_llm and self.llm_service:
|
||
self.logger.info(f"Attempting LLM generation for path parameters in '{operation_id}'")
|
||
path_schema, path_model_name = self._build_object_schema_for_params(path_param_specs, f"{operation_id}_PathParams")
|
||
if path_schema:
|
||
llm_path_params = self.llm_service.generate_data_from_schema(
|
||
path_schema,
|
||
prompt_instruction=None,
|
||
max_tokens=256,
|
||
temperature=0.1
|
||
)
|
||
if llm_path_params:
|
||
initial_path_params = llm_path_params
|
||
else:
|
||
self.logger.warning(f"LLM failed to generate path params for '{operation_id}', fallback to default.")
|
||
else:
|
||
self.logger.warning(f"Failed to build schema for path params in '{operation_id}', fallback to default.")
|
||
if not initial_path_params: # fallback
|
||
for param_spec in path_param_specs:
|
||
name = param_spec.get('name')
|
||
if not name: continue
|
||
if 'example' in param_spec:
|
||
initial_path_params[name] = param_spec['example']
|
||
elif param_spec.get('schema') and 'example' in param_spec['schema']:
|
||
initial_path_params[name] = param_spec['schema']['example'] # OpenAPI 3.0 `parameter.schema.example`
|
||
elif 'default' in param_spec.get('schema', {}):
|
||
initial_path_params[name] = param_spec['schema']['default']
|
||
elif 'default' in param_spec: # OpenAPI 2.0 `parameter.default`
|
||
initial_path_params[name] = param_spec['default']
|
||
else:
|
||
schema = param_spec.get('schema', {})
|
||
param_type = schema.get('type', 'string')
|
||
if param_type == 'integer': initial_path_params[name] = 123
|
||
elif param_type == 'number': initial_path_params[name] = 1.23
|
||
elif param_type == 'boolean': initial_path_params[name] = True
|
||
elif param_type == 'string' and schema.get('format') == 'uuid': initial_path_params[name] = str(UUID(int=0)) # Example UUID
|
||
elif param_type == 'string' and schema.get('format') == 'date': initial_path_params[name] = dt.date.today().isoformat()
|
||
elif param_type == 'string' and schema.get('format') == 'date-time': initial_path_params[name] = dt.datetime.now().isoformat()
|
||
else: initial_path_params[name] = f"param_{name}"
|
||
self.logger.debug(f"Initial path param for '{operation_id}': {initial_path_params}")
|
||
|
||
# 2. 处理查询参数
|
||
query_param_specs = [p for p in parameters if p.get('in') == 'query']
|
||
if query_param_specs:
|
||
should_use_llm = self._should_use_llm_for_param_type("query_params", test_case_instance)
|
||
if should_use_llm and self.llm_service:
|
||
self.logger.info(f"Attempting LLM generation for query parameters in '{operation_id}'")
|
||
query_schema, query_model_name = self._build_object_schema_for_params(query_param_specs, f"{operation_id}_QueryParams")
|
||
if query_schema:
|
||
llm_query_params = self.llm_service.generate_data_from_schema(
|
||
query_schema,
|
||
prompt_instruction=None,
|
||
max_tokens=512,
|
||
temperature=0.1
|
||
)
|
||
if llm_query_params:
|
||
initial_query_params = llm_query_params
|
||
else:
|
||
self.logger.warning(f"LLM failed to generate query params for '{operation_id}', fallback to default.")
|
||
if not initial_query_params: # fallback
|
||
for param_spec in query_param_specs:
|
||
name = param_spec.get('name')
|
||
if not name: continue
|
||
if 'example' in param_spec:
|
||
initial_query_params[name] = param_spec['example']
|
||
elif param_spec.get('schema') and 'example' in param_spec['schema']:
|
||
initial_query_params[name] = param_spec['schema']['example']
|
||
elif 'default' in param_spec.get('schema', {}):
|
||
initial_query_params[name] = param_spec['schema']['default']
|
||
elif 'default' in param_spec:
|
||
initial_query_params[name] = param_spec['default']
|
||
else:
|
||
initial_query_params[name] = f"query_val_{name}"
|
||
self.logger.debug(f"Initial query param for '{operation_id}': {initial_query_params}")
|
||
|
||
# 3. 处理请求头参数 (包括规范定义的和标准的 Content-Type/Accept)
|
||
header_param_specs = [p for p in parameters if p.get('in') == 'header']
|
||
custom_header_param_specs = [p for p in header_param_specs if p.get('name', '').lower() not in ['content-type', 'accept', 'authorization']]
|
||
if custom_header_param_specs:
|
||
should_use_llm = self._should_use_llm_for_param_type("headers", test_case_instance)
|
||
if should_use_llm and self.llm_service:
|
||
self.logger.info(f"Attempting LLM generation for header parameters in '{operation_id}'")
|
||
header_schema, header_model_name = self._build_object_schema_for_params(custom_header_param_specs, f"{operation_id}_HeaderParams")
|
||
if header_schema:
|
||
llm_header_params = self.llm_service.generate_data_from_schema(
|
||
header_schema,
|
||
prompt_instruction=None,
|
||
max_tokens=256,
|
||
temperature=0.1
|
||
)
|
||
if llm_header_params:
|
||
for k, v in llm_header_params.items():
|
||
initial_headers[k] = str(v)
|
||
else:
|
||
self.logger.warning(f"LLM failed to generate header params for '{operation_id}', fallback to default.")
|
||
if not any(k for k in initial_headers if k.lower() not in ['content-type', 'accept', 'authorization']): # fallback
|
||
for param_spec in custom_header_param_specs:
|
||
name = param_spec.get('name')
|
||
if not name: continue
|
||
if 'example' in param_spec:
|
||
initial_headers[name] = str(param_spec['example'])
|
||
elif param_spec.get('schema') and 'example' in param_spec['schema']:
|
||
initial_headers[name] = str(param_spec['schema']['example'])
|
||
elif 'default' in param_spec.get('schema', {}):
|
||
initial_headers[name] = str(param_spec['schema']['default'])
|
||
elif 'default' in param_spec:
|
||
initial_headers[name] = str(param_spec['default'])
|
||
else:
|
||
initial_headers[name] = f"header_val_{name}"
|
||
self.logger.debug(f"Initial custom header param for '{operation_id}': {initial_headers}")
|
||
|
||
# 3.1 设置 Content-Type
|
||
# 优先从 requestBody.content 获取 (OpenAPI 3.x)
|
||
request_body_spec_candidate = endpoint_spec.get('requestBody')
|
||
request_body_spec = request_body_spec_candidate if isinstance(request_body_spec_candidate, dict) else {}
|
||
|
||
if 'content' in request_body_spec:
|
||
content_types = list(request_body_spec['content'].keys())
|
||
if content_types:
|
||
# 优先选择 application/json 如果存在
|
||
initial_headers['Content-Type'] = next((ct for ct in content_types if 'json' in ct.lower()), content_types[0])
|
||
elif 'consumes' in endpoint_spec: # 然后是 consumes (OpenAPI 2.0)
|
||
consumes = endpoint_spec['consumes']
|
||
if consumes:
|
||
initial_headers['Content-Type'] = next((c for c in consumes if 'json' in c.lower()), consumes[0])
|
||
elif method in ['POST', 'PUT', 'PATCH'] and not initial_headers.get('Content-Type'):
|
||
initial_headers['Content-Type'] = 'application/json' # 默认对于这些方法
|
||
self.logger.debug(f"Initial Content-Type for '{operation_id}': {initial_headers.get('Content-Type')}")
|
||
|
||
# 3.2 设置 Accept
|
||
# 优先从 responses.<code>.content 获取 (OpenAPI 3.x)
|
||
responses_spec = endpoint_spec.get('responses', {})
|
||
accept_header_set = False
|
||
for code, response_def in responses_spec.items():
|
||
if 'content' in response_def:
|
||
accept_types = list(response_def['content'].keys())
|
||
if accept_types:
|
||
initial_headers['Accept'] = next((at for at in accept_types if 'json' in at.lower() or '*/*' in at), accept_types[0])
|
||
accept_header_set = True
|
||
break
|
||
if not accept_header_set and 'produces' in endpoint_spec: # 然后是 produces (OpenAPI 2.0)
|
||
produces = endpoint_spec['produces']
|
||
if produces:
|
||
initial_headers['Accept'] = next((p for p in produces if 'json' in p.lower() or '*/*' in p), produces[0])
|
||
accept_header_set = True
|
||
if not accept_header_set and not initial_headers.get('Accept'):
|
||
initial_headers['Accept'] = 'application/json, */*' # 更通用的默认值
|
||
self.logger.debug(f"Initial Accept header for '{operation_id}': {initial_headers.get('Accept')}")
|
||
|
||
# 4. 处理请求体 (Body)
|
||
request_body_schema: Optional[Dict[str, Any]] = None
|
||
# 确定请求体 schema 的来源，优先 OpenAPI 3.x 的 requestBody
|
||
content_type_for_body_schema = initial_headers.get('Content-Type', 'application/json').split(';')[0].strip()
|
||
|
||
if 'content' in request_body_spec and content_type_for_body_schema in request_body_spec['content']:
|
||
request_body_schema = request_body_spec['content'][content_type_for_body_schema].get('schema')
|
||
elif 'parameters' in endpoint_spec: # OpenAPI 2.0 (Swagger) body parameter
|
||
body_param = next((p for p in parameters if p.get('in') == 'body'), None)
|
||
if body_param and 'schema' in body_param:
|
||
request_body_schema = body_param['schema']
|
||
|
||
if request_body_schema:
|
||
should_use_llm_for_body = self._should_use_llm_for_param_type("body", test_case_instance)
|
||
if should_use_llm_for_body and self.llm_service:
|
||
self.logger.info(f"Attempting LLM generation for request body of '{operation_id}' with schema...")
|
||
initial_body = self.llm_service.generate_data_from_schema(
|
||
request_body_schema,
|
||
prompt_instruction=None, # 如有自定义指令可替换
|
||
max_tokens=1024,
|
||
temperature=0.1
|
||
)
|
||
if initial_body is None:
|
||
self.logger.warning(f"LLM failed to generate request body for '{operation_id}'. Falling back to default schema generator.")
|
||
initial_body = self._generate_data_from_schema(request_body_schema, context_name=f"{operation_id}_body", operation_id=operation_id)
|
||
else:
|
||
initial_body = self._generate_data_from_schema(request_body_schema, context_name=f"{operation_id}_body", operation_id=operation_id)
|
||
self.logger.debug(f"Initial request body generated for '{operation_id}' (type: {type(initial_body)})")
|
||
else:
|
||
self.logger.debug(f"No request body schema found or applicable for '{operation_id}' with Content-Type '{content_type_for_body_schema}'. Initial body is None.")
|
||
|
||
# 构造并返回APIRequestContext
|
||
return APIRequestContext(
|
||
method=method,
|
||
url=path_template, # 传递路径模板, e.g. /items/{itemId}
|
||
path_params=initial_path_params,
|
||
query_params=initial_query_params,
|
||
headers=initial_headers,
|
||
body=initial_body,
|
||
endpoint_spec=endpoint_spec # 传递原始的 endpoint_spec 字典
|
||
)
|
||
|
||
def _build_object_schema_for_params(self, params_spec_list: List[Dict[str, Any]], model_name_base: str) -> Tuple[Optional[Dict[str, Any]], str]:
|
||
"""
|
||
将参数列表 (如路径参数、查询参数列表) 转换为一个单一的 "type: object" JSON schema，
|
||
以便用于创建 Pydantic 模型。
|
||
会尝试适配参数定义中缺少嵌套 'schema' 字段但有顶层 'type' 的情况。
|
||
"""
|
||
if not params_spec_list:
|
||
return None, model_name_base
|
||
|
||
properties = {}
|
||
required_params = []
|
||
|
||
parameter_names = []
|
||
for param_spec in params_spec_list:
|
||
param_name = param_spec.get("name")
|
||
if not param_name:
|
||
self.logger.warning(f"参数定义缺少 'name' 字段: {param_spec}。已跳过。")
|
||
continue
|
||
parameter_names.append(param_name)
|
||
|
||
param_schema = param_spec.get("schema")
|
||
|
||
# ---- 适配开始 ----
|
||
if not param_schema and param_spec.get("type"):
|
||
self.logger.debug(f"参数 '{param_name}' 缺少嵌套 'schema' 字段，尝试从顶层 'type' 构建临时schema。 Param spec: {param_spec}")
|
||
temp_schema = {"type": param_spec.get("type")}
|
||
# 从 param_spec 顶层提取其他相关字段到 temp_schema
|
||
for key in ["format", "default", "example", "description", "enum",
|
||
"minimum", "maximum", "minLength", "maxLength", "pattern",
|
||
"items"]: # items 用于处理顶层定义的array
|
||
if key in param_spec:
|
||
temp_schema[key] = param_spec[key]
|
||
param_schema = temp_schema
|
||
# ---- 适配结束 ----
|
||
|
||
if not param_schema: # 如果适配后仍然没有schema
|
||
self.logger.warning(f"参数 '{param_name}' 缺少 'schema' 定义且无法从顶层构建: {param_spec}。已跳过。")
|
||
continue
|
||
|
||
# 处理 $ref (简单情况，假设ref在components.schemas)
|
||
# 更复杂的 $ref 解析可能需要访问完整的OpenAPI文档
|
||
if isinstance(param_schema, dict) and "$ref" in param_schema: # 确保 param_schema 是字典再检查 $ref
|
||
ref_path = param_schema["$ref"]
|
||
# 这是一个非常简化的$ref处理，实际可能需要解析整个文档
|
||
self.logger.warning(f"参数 '{param_name}' 的 schema 包含 $ref '{ref_path}'，当前不支持自动解析。请确保schema是内联的。")
|
||
# 可以尝试提供一个非常基础的schema，或者跳过这个参数，或者让_generate_data_from_schema处理
|
||
properties[param_name] = {"type": "string", "description": f"Reference to {ref_path}"}
|
||
elif isinstance(param_schema, dict): # 确保 param_schema 是字典
|
||
properties[param_name] = param_schema
|
||
else:
|
||
self.logger.warning(f"参数 '{param_name}' 的 schema 不是一个有效的字典: {param_schema}。已跳过。")
|
||
continue
|
||
|
||
if param_spec.get("required", False):
|
||
required_params.append(param_name)
|
||
|
||
if not properties: # 如果所有参数都无效
|
||
return None, model_name_base
|
||
|
||
model_name = f"{model_name_base}_{'_'.join(sorted(parameter_names))}" # 使模型名更具唯一性
|
||
|
||
object_schema = {
|
||
"type": "object",
|
||
"properties": properties,
|
||
}
|
||
if required_params:
|
||
object_schema["required"] = required_params
|
||
|
||
self.logger.debug(f"[{model_name_base}] 为参数集 {parameter_names} 构建的最终 Object Schema: {json.dumps(object_schema, indent=2)}, 模型名: {model_name}")
|
||
return object_schema, model_name
|
||
|
||
def _generate_params_from_list(self, params_spec_list: List[Dict[str, Any]], operation_id: str, param_type: str) -> Dict[str, Any]:
|
||
"""
|
||
遍历参数定义列表，使用 _generate_data_from_schema 为每个参数生成数据。
|
||
会尝试适配参数定义中缺少嵌套 'schema' 字段但有顶层 'type' 的情况。
|
||
"""
|
||
generated_params: Dict[str, Any] = {}
|
||
if not params_spec_list:
|
||
self.logger.info(f"[{operation_id}] 没有定义 {param_type} 参数。")
|
||
return generated_params
|
||
|
||
self.logger.info(f"[{operation_id}] 使用常规方法生成 {param_type} 参数。")
|
||
for param_spec in params_spec_list:
|
||
param_name = param_spec.get("name")
|
||
param_schema = param_spec.get("schema")
|
||
|
||
# ---- 适配开始 ----
|
||
if not param_schema and param_spec.get("type"):
|
||
self.logger.debug(f"参数 '{param_name}' ('{param_type}' 类型) 缺少嵌套 'schema' 字段，尝试从顶层 'type' 构建临时schema用于常规生成。 Param spec: {param_spec}")
|
||
temp_schema = {"type": param_spec.get("type")}
|
||
# 从 param_spec 顶层提取其他相关字段到 temp_schema
|
||
for key in ["format", "default", "example", "description", "enum",
|
||
"minimum", "maximum", "minLength", "maxLength", "pattern",
|
||
"items"]: # items 用于处理顶层定义的array
|
||
if key in param_spec:
|
||
temp_schema[key] = param_spec[key]
|
||
param_schema = temp_schema
|
||
# ---- 适配结束 ----
|
||
|
||
if param_name and param_schema and isinstance(param_schema, dict): # 确保param_schema是字典
|
||
generated_value = self._generate_data_from_schema(
|
||
param_schema,
|
||
context_name=f"{param_type} parameter '{param_name}'",
|
||
operation_id=operation_id
|
||
)
|
||
if generated_value is not None:
|
||
generated_params[param_name] = generated_value
|
||
elif param_spec.get("required"):
|
||
self.logger.warning(f"[{operation_id}] 未能为必需的 {param_type} 参数 '{param_name}' 生成数据 (schema: {param_schema})，且其 schema 中可能没有有效的默认值或示例。")
|
||
else:
|
||
self.logger.warning(f"[{operation_id}] 跳过无效的 {param_type} 参数定义 (名称: {param_name}, schema: {param_schema}): {param_spec}")
|
||
self.logger.info(f"[{operation_id}] 常规方法生成的 {param_type} 参数: {generated_params}")
|
||
return generated_params
|
||
|
||
def run_test_for_endpoint(self, endpoint: Union[YAPIEndpoint, SwaggerEndpoint],
|
||
global_api_spec: Union[ParsedYAPISpec, ParsedSwaggerSpec]
|
||
) -> TestResult:
|
||
endpoint_id = f"{getattr(endpoint, 'method', 'GET').upper()} {getattr(endpoint, 'path', '/')}"
|
||
endpoint_name = getattr(endpoint, 'title', '') or getattr(endpoint, 'summary', '') or endpoint_id
|
||
|
||
self.logger.info(f"开始为端点测试: {endpoint_id} ({endpoint_name})")
|
||
|
||
endpoint_test_result = TestResult(
|
||
endpoint_id=endpoint_id,
|
||
endpoint_name=endpoint_name,
|
||
)
|
||
|
||
if not self.test_case_registry:
|
||
self.logger.warning(f"TestCaseRegistry 未初始化，无法为端点 '{endpoint_id}' 执行自定义测试用例。")
|
||
endpoint_test_result.overall_status = TestResult.Status.SKIPPED
|
||
endpoint_test_result.error_message = "TestCaseRegistry 未初始化。"
|
||
endpoint_test_result.finalize_endpoint_test()
|
||
return endpoint_test_result
|
||
|
||
applicable_test_case_classes_unordered = self.test_case_registry.get_applicable_test_cases(
|
||
endpoint_method=endpoint.method.upper(),
|
||
endpoint_path=endpoint.path
|
||
)
|
||
|
||
if not applicable_test_case_classes_unordered:
|
||
self.logger.info(f"端点 '{endpoint_id}' 没有找到适用的自定义测试用例。")
|
||
endpoint_test_result.finalize_endpoint_test() # 确保在返回前调用
|
||
return endpoint_test_result
|
||
|
||
# 根据 execution_order 排序测试用例
|
||
applicable_test_case_classes = sorted(
|
||
applicable_test_case_classes_unordered,
|
||
key=lambda tc_class: tc_class.execution_order
|
||
)
|
||
|
||
self.logger.info(f"端点 '{endpoint_id}' 发现了 {len(applicable_test_case_classes)} 个适用的测试用例 (已排序): {[tc.id for tc in applicable_test_case_classes]}")
|
||
|
||
critical_setup_test_failed = False
|
||
critical_setup_failure_reason = ""
|
||
|
||
for tc_class in applicable_test_case_classes:
|
||
start_single_tc_time = time.monotonic() # 用于计算跳过测试用例的持续时间
|
||
|
||
if critical_setup_test_failed:
|
||
self.logger.warning(f"由于关键的前置测试用例失败，跳过测试用例 '{tc_class.id}' for '{endpoint_id}'. 原因: {critical_setup_failure_reason}")
|
||
skipped_tc_duration = time.monotonic() - start_single_tc_time
|
||
executed_case_result = ExecutedTestCaseResult(
|
||
test_case_id=tc_class.id,
|
||
test_case_name=tc_class.name,
|
||
test_case_severity=tc_class.severity,
|
||
status=ExecutedTestCaseResult.Status.SKIPPED,
|
||
validation_points=[],
|
||
message=f"由于关键的前置测试失败而被跳过: {critical_setup_failure_reason}",
|
||
duration=skipped_tc_duration
|
||
)
|
||
else:
|
||
self.logger.debug(f"准备执行测试用例 '{tc_class.id}' for '{endpoint_id}'")
|
||
executed_case_result = self._execute_single_test_case(
|
||
test_case_class=tc_class,
|
||
endpoint_spec=endpoint,
|
||
global_api_spec=global_api_spec
|
||
)
|
||
|
||
# 检查是否是关键测试用例以及是否失败
|
||
if hasattr(tc_class, 'is_critical_setup_test') and tc_class.is_critical_setup_test:
|
||
if executed_case_result.status in [ExecutedTestCaseResult.Status.FAILED, ExecutedTestCaseResult.Status.ERROR]:
|
||
critical_setup_test_failed = True
|
||
critical_setup_failure_reason = f"关键测试 '{tc_class.id}' 失败 (状态: {executed_case_result.status.value})。消息: {executed_case_result.message}"
|
||
self.logger.error(f"关键的前置测试用例 '{tc_class.id}' for '{endpoint_id}' 失败。后续测试将被跳过。原因: {critical_setup_failure_reason}")
|
||
|
||
endpoint_test_result.add_executed_test_case_result(executed_case_result)
|
||
|
||
# 日志部分可以保持不变或根据需要调整
|
||
if executed_case_result.status.value == ExecutedTestCaseResult.Status.FAILED.value:
|
||
self.logger.debug(f"\033[91m ❌ 测试用例 '{tc_class.id}' 执行失败。\033[0m")
|
||
elif executed_case_result.status.value == ExecutedTestCaseResult.Status.PASSED.value :
|
||
self.logger.debug(f"\033[92m ✅ 测试用例 '{tc_class.id}' 执行成功。\033[0m")
|
||
# 对于SKIPPED和ERROR状态，可以添加不同颜色的日志
|
||
elif executed_case_result.status.value == ExecutedTestCaseResult.Status.SKIPPED.value:
|
||
self.logger.debug(f"\033[93m ⏭️ 测试用例 '{tc_class.id}' 被跳过。\033[0m") # 黄色
|
||
elif executed_case_result.status.value == ExecutedTestCaseResult.Status.ERROR.value:
|
||
self.logger.debug(f"\033[91m 💥 测试用例 '{tc_class.id}' 执行时发生错误。\033[0m") # 红色 (与FAILED相同或不同)
|
||
|
||
self.logger.debug(f"测试用例 '{tc_class.id}' 执行完毕，状态: {executed_case_result.status.value}")
|
||
|
||
endpoint_test_result.finalize_endpoint_test()
|
||
self.logger.info(f"端点 '{endpoint_id}' 测试完成，最终状态: {endpoint_test_result.overall_status.value}")
|
||
|
||
return endpoint_test_result
|
||
|
||
def run_tests_from_yapi(self, yapi_file_path: str,
|
||
categories: Optional[List[str]] = None,
|
||
custom_test_cases_dir: Optional[str] = None
|
||
) -> Tuple[TestSummary, Optional[ParsedAPISpec]]:
|
||
self.logger.info(f"准备从YAPI文件运行测试用例: {yapi_file_path}")
|
||
self.api_call_details_log = [] # 为新的测试用例运行重置API调用日志
|
||
|
||
parsed_yapi = self.parser.parse_yapi_spec(yapi_file_path)
|
||
summary = TestSummary()
|
||
|
||
if not parsed_yapi:
|
||
self.logger.error(f"解析YAPI文件失败: {yapi_file_path}")
|
||
summary.finalize_summary() # 即使失败也最终化摘要
|
||
return summary, None
|
||
|
||
# 调用内部执行方法来执行测试用例
|
||
self._execute_tests_from_parsed_spec(
|
||
parsed_spec=parsed_yapi,
|
||
summary=summary,
|
||
categories=categories,
|
||
custom_test_cases_dir=custom_test_cases_dir
|
||
)
|
||
# finalize_summary 和 print_summary_to_console 将在 run_api_tests.py 中进行
|
||
return summary, parsed_yapi
|
||
|
||
def run_tests_from_swagger(self, swagger_file_path: str,
|
||
tags: Optional[List[str]] = None,
|
||
custom_test_cases_dir: Optional[str] = None
|
||
) -> Tuple[TestSummary, Optional[ParsedAPISpec]]:
|
||
self.logger.info(f"准备从Swagger文件运行测试用例: {swagger_file_path}")
|
||
self.api_call_details_log = [] # 为新的测试用例运行重置API调用日志
|
||
|
||
parsed_swagger = self.parser.parse_swagger_spec(swagger_file_path)
|
||
summary = TestSummary()
|
||
|
||
if not parsed_swagger:
|
||
self.logger.error(f"解析Swagger文件失败: {swagger_file_path}")
|
||
summary.finalize_summary()
|
||
return summary, None
|
||
|
||
# 调用内部执行方法来执行测试用例
|
||
self._execute_tests_from_parsed_spec(
|
||
parsed_spec=parsed_swagger,
|
||
summary=summary,
|
||
tags=tags,
|
||
custom_test_cases_dir=custom_test_cases_dir
|
||
)
|
||
# finalize_summary 和 print_summary_to_console 将在 run_api_tests.py 中进行
|
||
return summary, parsed_swagger
|
||
|
||
def _generate_data_from_schema(self, schema: Dict[str, Any],
|
||
context_name: Optional[str] = None,
|
||
operation_id: Optional[str] = None) -> Any:
|
||
"""
|
||
根据JSON Schema生成测试数据 (此方法基本保持不变，可能被测试用例或编排器内部使用)
|
||
增加了 context_name 和 operation_id 用于更详细的日志。
|
||
"""
|
||
log_prefix = f"[{operation_id}] " if operation_id else ""
|
||
context_log = f" (context: {context_name})" if context_name else ""
|
||
|
||
if not schema or not isinstance(schema, dict):
|
||
self.logger.debug(f"{log_prefix}_generate_data_from_schema: 提供的 schema 无效或为空{context_log}: {schema}")
|
||
return None
|
||
|
||
schema_type = schema.get('type')
|
||
|
||
if 'example' in schema:
|
||
self.logger.debug(f"{log_prefix}使用 schema 中的 'example' 值 for{context_log}: {schema['example']}")
|
||
return schema['example']
|
||
if 'default' in schema:
|
||
self.logger.debug(f"{log_prefix}使用 schema 中的 'default' 值 for{context_log}: {schema['default']}")
|
||
return schema['default']
|
||
|
||
if schema_type == 'object':
|
||
result = {}
|
||
properties = schema.get('properties', {})
|
||
self.logger.debug(f"{log_prefix}生成 object 类型数据 for{context_log}. Properties: {list(properties.keys())}")
|
||
for prop_name, prop_schema in properties.items():
|
||
# 递归调用时传递上下文，但稍微修改一下 context_name
|
||
nested_context = f"{context_name}.{prop_name}" if context_name else prop_name
|
||
result[prop_name] = self._generate_data_from_schema(prop_schema, nested_context, operation_id)
|
||
return result if result else {}
|
||
|
||
elif schema_type == 'array':
|
||
items_schema = schema.get('items', {})
|
||
min_items = schema.get('minItems', 1 if schema.get('default') is None and schema.get('example') is None else 0)
|
||
self.logger.debug(f"{log_prefix}生成 array 类型数据 for{context_log}. Items schema: {items_schema}, minItems: {min_items}")
|
||
if min_items == 0 and (schema.get('default') == [] or schema.get('example') == []):
|
||
return []
|
||
|
||
num_items_to_generate = max(1, min_items)
|
||
generated_array = []
|
||
for i in range(num_items_to_generate):
|
||
item_context = f"{context_name}[{i}]" if context_name else f"array_item[{i}]"
|
||
generated_array.append(self._generate_data_from_schema(items_schema, item_context, operation_id))
|
||
return generated_array
|
||
|
||
elif schema_type == 'string':
|
||
string_format = schema.get('format', '')
|
||
val = None
|
||
if 'enum' in schema and schema['enum']:
|
||
val = schema['enum'][0]
|
||
elif string_format == 'date': val = '2023-01-01'
|
||
elif string_format == 'date-time': val = datetime.datetime.now().isoformat()
|
||
elif string_format == 'email': val = 'test@example.com'
|
||
elif string_format == 'uuid': import uuid; val = str(uuid.uuid4())
|
||
else: val = 'example_string'
|
||
self.logger.debug(f"{log_prefix}生成 string 类型数据 ('{string_format}') for{context_log}: {val}")
|
||
return val
|
||
|
||
elif schema_type == 'number' or schema_type == 'integer':
|
||
val_to_return = schema.get('default', schema.get('example'))
|
||
if val_to_return is not None:
|
||
self.logger.debug(f"{log_prefix}使用 number/integer 的 default/example 值 for{context_log}: {val_to_return}")
|
||
return val_to_return
|
||
|
||
minimum = schema.get('minimum')
|
||
# maximum = schema.get('maximum') # Not used yet for generation, but could be
|
||
if minimum is not None:
|
||
val_to_return = minimum
|
||
else:
|
||
val_to_return = 0 if schema_type == 'integer' else 0.0
|
||
self.logger.debug(f"{log_prefix}生成 number/integer 类型数据 for{context_log}: {val_to_return}")
|
||
return val_to_return
|
||
|
||
elif schema_type == 'boolean':
|
||
val = schema.get('default', schema.get('example', False))
|
||
self.logger.debug(f"{log_prefix}生成 boolean 类型数据 for{context_log}: {val}")
|
||
return val
|
||
|
||
elif schema_type == 'null':
|
||
self.logger.debug(f"{log_prefix}生成 null 类型数据 for{context_log}")
|
||
return None
|
||
|
||
self.logger.debug(f"{log_prefix}_generate_data_from_schema: 未知或不支持的 schema 类型 '{schema_type}' for{context_log}. Schema: {schema}")
|
||
return None
|
||
|
||
def _format_url_with_path_params(self, path_template: str, path_params: Dict[str, Any]) -> str:
|
||
"""
|
||
使用提供的路径参数格式化URL路径模板。
|
||
例如: path_template='/users/{userId}/items/{itemId}', path_params={'userId': 123, 'itemId': 'abc'}
|
||
会返回 '/users/123/items/abc'
|
||
同时处理 base_url.
|
||
"""
|
||
# 首先确保 path_template 不以 '/' 开头，如果 self.base_url 已经以 '/' 结尾
|
||
# 或者确保它们之间只有一个 '/'
|
||
formatted_path = path_template
|
||
for key, value in path_params.items():
|
||
placeholder = f"{{{key}}}"
|
||
if placeholder in formatted_path:
|
||
formatted_path = formatted_path.replace(placeholder, str(value))
|
||
else:
|
||
self.logger.warning(f"路径参数 '{key}' 在路径模板 '{path_template}' 中未找到占位符。")
|
||
|
||
# 拼接 base_url 和格式化后的路径
|
||
# 确保 base_url 和 path 之间只有一个斜杠
|
||
if self.base_url.endswith('/') and formatted_path.startswith('/'):
|
||
url = self.base_url + formatted_path[1:]
|
||
elif not self.base_url.endswith('/') and not formatted_path.startswith('/'):
|
||
if formatted_path: # 避免在 base_url 后添加不必要的 '/' (如果 formatted_path 为空)
|
||
url = self.base_url + '/' + formatted_path
|
||
else:
|
||
url = self.base_url
|
||
else:
|
||
url = self.base_url + formatted_path
|
||
return url
|
||
|
||
def _resolve_json_schema_references(self, schema_to_resolve: Any, full_api_spec: Dict[str, Any], max_depth=10, current_depth=0) -> Any:
|
||
"""
|
||
递归解析JSON Schema中的$ref引用。
|
||
Args:
|
||
schema_to_resolve: 当前需要解析的schema部分 (可以是字典、列表或基本类型)。
|
||
full_api_spec: 完整的API规范字典，用于查找$ref路径。
|
||
max_depth: 最大递归深度，防止无限循环。
|
||
current_depth: 当前递归深度。
|
||
Returns:
|
||
解析了$ref的schema部分。
|
||
"""
|
||
if current_depth > max_depth:
|
||
self.logger.warning(f"达到最大$ref解析深度 ({max_depth})，可能存在循环引用。停止进一步解析。")
|
||
return schema_to_resolve
|
||
|
||
if isinstance(schema_to_resolve, dict):
|
||
if "$ref" in schema_to_resolve:
|
||
ref_path = schema_to_resolve["$ref"]
|
||
if not isinstance(ref_path, str) or not ref_path.startswith("#/"):
|
||
self.logger.warning(f"不支持的$ref格式或外部引用: {ref_path}。仅支持本地引用 (e.g., #/components/schemas/MyModel)。")
|
||
return schema_to_resolve # 或者根据需要返回错误/None
|
||
|
||
path_parts = ref_path[2:].split('/') # Remove '#/' and split
|
||
resolved_component = full_api_spec
|
||
try:
|
||
for part in path_parts:
|
||
if isinstance(resolved_component, list): # Handle paths like #/components/parameters/0
|
||
part = int(part)
|
||
resolved_component = resolved_component[part]
|
||
|
||
# 递归解析引用过来的组件，以处理嵌套的$ref
|
||
# 同时传递原始$ref携带的其他属性（如description, nullable等可以覆盖引用的内容）
|
||
# See: https://json-schema.org/understanding-json-schema/structuring.html#merging
|
||
# For simplicity here, we prioritize the resolved component, but a more robust solution
|
||
# would merge properties from the $ref object itself with the resolved one.
|
||
|
||
# Create a copy of the resolved component to avoid modifying the original spec
|
||
# and to allow merging of sibling keywords if any.
|
||
component_copy = copy.deepcopy(resolved_component)
|
||
|
||
# Merge sibling keywords from the $ref object into the resolved component.
|
||
# Keywords in the $ref object override those in the referenced schema.
|
||
merged_schema = component_copy
|
||
if isinstance(component_copy, dict): # Ensure it's a dict before trying to update
|
||
for key, value in schema_to_resolve.items():
|
||
if key != "$ref":
|
||
merged_schema[key] = value # Override or add
|
||
|
||
self.logger.debug(f"成功解析并合并 $ref: '{ref_path}'。正在递归解析其内容。")
|
||
return self._resolve_json_schema_references(merged_schema, full_api_spec, max_depth, current_depth + 1)
|
||
|
||
except (KeyError, IndexError, TypeError, ValueError) as e:
|
||
self.logger.error(f"解析$ref '{ref_path}' 失败: {e}.路径部分: {path_parts}. 当前组件类型: {type(resolved_component)}", exc_info=True)
|
||
return schema_to_resolve # 返回原始的$ref对象或错误指示
|
||
|
||
# 如果不是$ref，则递归处理字典中的每个值
|
||
# 使用copy避免在迭代时修改字典
|
||
resolved_dict = {}
|
||
for key, value in schema_to_resolve.items():
|
||
resolved_dict[key] = self._resolve_json_schema_references(value, full_api_spec, max_depth, current_depth + 1)
|
||
return resolved_dict
|
||
|
||
elif isinstance(schema_to_resolve, list):
|
||
# 递归处理列表中的每个元素
|
||
return [self._resolve_json_schema_references(item, full_api_spec, max_depth, current_depth + 1) for item in schema_to_resolve]
|
||
|
||
else:
|
||
# 基本类型 (string, number, boolean, null) 不需要解析
|
||
return schema_to_resolve
|
||
|
||
def _util_find_removable_field_path_recursive(self, current_schema: Dict[str, Any], current_path: List[str], full_api_spec_for_refs: Dict[str, Any]) -> Optional[List[Union[str, int]]]:
|
||
"""
|
||
(框架辅助方法) 递归查找第一个可移除的必填字段的路径。
|
||
此方法现在需要 full_api_spec_for_refs 以便在需要时解析 $ref。
|
||
"""
|
||
# 首先解析当前 schema，以防它是 $ref
|
||
resolved_schema = self._resolve_json_schema_references(current_schema, full_api_spec_for_refs)
|
||
|
||
if not isinstance(resolved_schema, dict) or resolved_schema.get("type") != "object":
|
||
return None
|
||
|
||
required_fields_at_current_level = resolved_schema.get("required", [])
|
||
properties = resolved_schema.get("properties", {})
|
||
self.logger.debug(f"[Util] 递归查找路径: {current_path}, 当前层级必填字段: {required_fields_at_current_level}, 属性: {list(properties.keys())}")
|
||
|
||
# 策略1: 查找当前层级直接声明的必填字段
|
||
if required_fields_at_current_level and properties:
|
||
for field_name in required_fields_at_current_level:
|
||
if field_name in properties:
|
||
self.logger.info(f"[Util] 策略1: 在路径 {'.'.join(map(str,current_path)) if current_path else 'root'} 找到可直接移除的必填字段: '{field_name}'")
|
||
return current_path + [field_name]
|
||
|
||
# 策略2: 查找数组属性，看其内部item是否有必填字段
|
||
if properties:
|
||
for prop_name, prop_schema_orig in properties.items():
|
||
prop_schema = self._resolve_json_schema_references(prop_schema_orig, full_api_spec_for_refs)
|
||
if isinstance(prop_schema, dict) and prop_schema.get("type") == "array":
|
||
items_schema_orig = prop_schema.get("items")
|
||
if isinstance(items_schema_orig, dict):
|
||
items_schema = self._resolve_json_schema_references(items_schema_orig, full_api_spec_for_refs)
|
||
if isinstance(items_schema, dict) and items_schema.get("type") == "object":
|
||
item_required_fields = items_schema.get("required", [])
|
||
item_properties = items_schema.get("properties", {})
|
||
if item_required_fields and item_properties:
|
||
first_required_field_in_item = next((rf for rf in item_required_fields if rf in item_properties), None)
|
||
if first_required_field_in_item:
|
||
self.logger.info(f"[Util] 策略2: 在数组属性 '{prop_name}' (路径 {'.'.join(map(str,current_path)) if current_path else 'root'}) 的元素内找到必填字段: '{first_required_field_in_item}'. 路径: {current_path + [prop_name, 0, first_required_field_in_item]}")
|
||
return current_path + [prop_name, 0, first_required_field_in_item]
|
||
|
||
# 策略3: 递归到子对象中查找（可选，但对于通用工具可能有用）
|
||
# 注意：这可能会找到非顶层必填对象内部的必填字段。
|
||
# if properties:
|
||
# for prop_name, prop_schema_orig_for_recurse in properties.items():
|
||
# prop_schema_for_recurse = self._resolve_json_schema_references(prop_schema_orig_for_recurse, full_api_spec_for_refs)
|
||
# if isinstance(prop_schema_for_recurse, dict) and prop_schema_for_recurse.get("type") == "object":
|
||
# # Avoid re-checking fields already covered by strategy 1 if they were required at this level
|
||
# # if prop_name not in required_fields_at_current_level:
|
||
# self.logger.debug(f"[Util] 策略3: 尝试递归进入对象属性 '{prop_name}' (路径 {current_path})")
|
||
# found_path_deeper = self._util_find_removable_field_path_recursive(prop_schema_for_recurse, current_path + [prop_name], full_api_spec_for_refs)
|
||
# if found_path_deeper:
|
||
# return found_path_deeper
|
||
|
||
self.logger.debug(f"[Util] 在路径 {'.'.join(map(str,current_path)) if current_path else 'root'} 未通过任何策略找到可移除的必填字段。")
|
||
return None
|
||
|
||
def _util_remove_value_at_path(self, data_container: Any, path: List[Union[str, int]]) -> Tuple[Any, Any, bool]:
|
||
"""
|
||
(框架辅助方法) 从嵌套的字典/列表中移除指定路径的值。
|
||
返回 (修改后的容器, 被移除的值, 是否成功)。
|
||
"""
|
||
if not path:
|
||
self.logger.error("[Util] _util_remove_value_at_path: 路径不能为空。")
|
||
return data_container, None, False
|
||
|
||
# 深拷贝以避免修改原始数据，除非调用者期望如此
|
||
# 如果 data_container 是 None 且路径非空，则尝试构建最小结构
|
||
if data_container is None:
|
||
if isinstance(path[0], str): # 路径以字段名开始，期望字典
|
||
container_copy = {}
|
||
elif isinstance(path[0], int): # 路径以索引开始，期望列表
|
||
container_copy = []
|
||
else:
|
||
self.logger.error(f"[Util] _util_remove_value_at_path: 路径的第一个元素 '{path[0]}' 类型未知。")
|
||
return data_container, None, False
|
||
else:
|
||
container_copy = copy.deepcopy(data_container)
|
||
|
||
current_level = container_copy
|
||
original_value = None
|
||
|
||
try:
|
||
for i, key_or_index in enumerate(path):
|
||
is_last_element = (i == len(path) - 1)
|
||
|
||
if is_last_element:
|
||
if isinstance(key_or_index, str): # Key for a dictionary (field name)
|
||
if isinstance(current_level, dict) and key_or_index in current_level:
|
||
original_value = current_level.pop(key_or_index)
|
||
self.logger.info(f"[Util] 从路径 '{'.'.join(map(str,path))}' 成功移除字段 '{key_or_index}' (原值: '{original_value}')。")
|
||
return container_copy, original_value, True
|
||
elif isinstance(current_level, dict):
|
||
self.logger.warning(f"[Util] 路径的最后一部分 '{key_or_index}' (string key) 在对象中未找到。路径: {'.'.join(map(str,path))}")
|
||
return container_copy, None, False # 字段不存在，但结构符合
|
||
else:
|
||
self.logger.error(f"[Util] 路径的最后一部分 '{key_or_index}' (string key) 期望父级是字典，但找到 {type(current_level)}。路径: {'.'.join(map(str,path))}")
|
||
return data_container, None, False # 结构不符，返回原始数据
|
||
else: # Last element of path is an index - this indicates removing an item from a list
|
||
if isinstance(current_level, list) and isinstance(key_or_index, int) and 0 <= key_or_index < len(current_level):
|
||
original_value = current_level.pop(key_or_index)
|
||
self.logger.info(f"[Util] 从路径 '{'.'.join(map(str,path))}' 成功移除索引 '{key_or_index}' 的元素 (原值: '{original_value}')。")
|
||
return container_copy, original_value, True
|
||
elif isinstance(current_level, list):
|
||
self.logger.warning(f"[Util] 路径的最后一部分索引 '{key_or_index}' 超出列表范围或类型不符。列表长度: {len(current_level)}. 路径: {'.'.join(map(str,path))}")
|
||
return container_copy, None, False # 索引无效，但结构符合
|
||
else:
|
||
self.logger.error(f"[Util] 路径的最后一部分 '{key_or_index}' 期望父级是列表，但找到 {type(current_level)}。路径: {'.'.join(map(str,path))}")
|
||
return data_container, None, False # 结构不符
|
||
|
||
else: # Not the last element, so we are traversing or building the structure
|
||
next_key_or_index = path[i+1]
|
||
|
||
if isinstance(key_or_index, str): # Current path part is a dictionary key
|
||
if not isinstance(current_level, dict):
|
||
self.logger.debug(f"[Util] 路径期望字典，但在 '{key_or_index}' (父级)处找到 {type(current_level)}. 将创建空字典。")
|
||
# This should only happen if current_level was initially part of a None container and we're building it up.
|
||
# If current_level is not a dict and it's not the root being built, it's an error.
|
||
# For robust path creation from None:
|
||
if current_level is container_copy and not container_copy : # building from scratch
|
||
current_level = {} # This change needs to be reflected in container_copy if this is the root
|
||
if i == 0: container_copy = current_level
|
||
else: # This case is complex: how to link back if not root?
|
||
self.logger.error(f"[Util] 无法在非根级别从非字典创建路径。")
|
||
return data_container, None, False
|
||
else: # Path expects dict, but found something else not at root.
|
||
self.logger.error(f"[Util] 路径期望字典，但在 '{key_or_index}' 处找到 {type(current_level)}。")
|
||
return data_container, None, False
|
||
|
||
# Ensure the next level exists and is of the correct type
|
||
if isinstance(next_key_or_index, int): # Next is an array index
|
||
if key_or_index not in current_level or not isinstance(current_level.get(key_or_index), list):
|
||
self.logger.debug(f"[Util] 路径 '{key_or_index}' 下需要列表 (为索引 {next_key_or_index} 做准备)，将创建空列表。")
|
||
current_level[key_or_index] = []
|
||
current_level = current_level[key_or_index]
|
||
else: # Next is a dictionary key
|
||
if key_or_index not in current_level or not isinstance(current_level.get(key_or_index), dict):
|
||
self.logger.debug(f"[Util] 路径 '{key_or_index}' 下需要字典 (为键 '{next_key_or_index}' 做准备)，将创建空字典。")
|
||
current_level[key_or_index] = {}
|
||
current_level = current_level[key_or_index]
|
||
|
||
elif isinstance(key_or_index, int): # Current path part is an array index
|
||
if not isinstance(current_level, list):
|
||
self.logger.error(f"[Util] 路径期望列表以应用索引 '{key_or_index}'，但找到 {type(current_level)}。")
|
||
return data_container, None, False
|
||
|
||
# Ensure the list is long enough, fill with dict/list based on next path element
|
||
while len(current_level) <= key_or_index:
|
||
if isinstance(next_key_or_index, str): # Next is a dict key
|
||
self.logger.debug(f"[Util] 数组在索引 {key_or_index} 处需要元素，将添加空字典。")
|
||
current_level.append({})
|
||
else: # Next is an array index
|
||
self.logger.debug(f"[Util] 数组在索引 {key_or_index} 处需要元素，将添加空列表。")
|
||
current_level.append([])
|
||
|
||
# Ensure the element at index is of the correct type for the next key/index
|
||
if isinstance(next_key_or_index, str): # Next is a dict key
|
||
if not isinstance(current_level[key_or_index], dict):
|
||
self.logger.debug(f"[Util] 数组项 at index {key_or_index} 需要是字典。将被替换。")
|
||
current_level[key_or_index] = {}
|
||
elif isinstance(next_key_or_index, int): # Next is an array index
|
||
if not isinstance(current_level[key_or_index], list):
|
||
self.logger.debug(f"[Util] 数组项 at index {key_or_index} 需要是列表。将被替换。")
|
||
current_level[key_or_index] = []
|
||
|
||
current_level = current_level[key_or_index]
|
||
|
||
else:
|
||
self.logger.error(f"[Util] 路径部分 '{key_or_index}' 类型未知 ({type(key_or_index)}).")
|
||
return data_container, None, False
|
||
except Exception as e:
|
||
self.logger.error(f"[Util] 在准备移除字段路径 '{'.'.join(map(str,path))}' 时发生错误: {e}", exc_info=True)
|
||
return data_container, None, False
|
||
|
||
self.logger.error(f"[Util] _util_remove_value_at_path 未能在循环内按预期返回。路径: {'.'.join(map(str,path))}")
|
||
return data_container, None, False
|
||
|
||
# --- 新增：场景测试执行相关方法 ---
|
||
def _resolve_value_from_context_or_literal(self, value_template: Any, stage_context: Dict[str, Any], step_name_for_log: str) -> Any:
|
||
"""
|
||
解析一个值，如果它是字符串且符合 {{stage_context.变量}} 格式，则从阶段上下文中取值，否则直接返回值。
|
||
支持从字典和列表中深入取值，例如 {{stage_context.user.id}} 或 {{stage_context.items[0].name}}。
|
||
"""
|
||
if isinstance(value_template, str):
|
||
match = re.fullmatch(r"\{\{\s*stage_context\.([a-zA-Z0-9_\.\[\]]+)\s*\}\}", value_template)
|
||
if match:
|
||
path_expression = match.group(1)
|
||
self.logger.debug(f"[阶段步骤 '{step_name_for_log}'] 解析上下文路径: '{path_expression}' 来自模板 '{value_template}'")
|
||
try:
|
||
current_value = stage_context
|
||
parts = re.split(r'\.(?![^\[]*\])|\[|\]', path_expression)
|
||
parts = [p for p in parts if p] # 移除空字符串
|
||
|
||
for part in parts:
|
||
if isinstance(current_value, dict):
|
||
current_value = current_value[part]
|
||
elif isinstance(current_value, list) and part.isdigit():
|
||
current_value = current_value[int(part)]
|
||
else:
|
||
raise KeyError(f"路径部分 '{part}' 无法从当前值类型 {type(current_value)} 中解析")
|
||
self.logger.info(f"[测试阶段步骤 '{step_name_for_log}'] 从上下文解析到值 '{current_value}' (路径: '{path_expression}')")
|
||
return current_value
|
||
except (KeyError, IndexError, TypeError) as e:
|
||
self.logger.error(f"[测试阶段步骤 '{step_name_for_log}'] 从阶段上下文解析路径 '{path_expression}' 失败: {e}", exc_info=True)
|
||
return None # 或抛出错误，或返回原始模板以提示错误
|
||
return value_template # 不是有效的占位符，返回原始字符串
|
||
elif isinstance(value_template, list):
|
||
return [self._resolve_value_from_context_or_literal(item, stage_context, step_name_for_log) for item in value_template]
|
||
elif isinstance(value_template, dict):
|
||
return {k: self._resolve_value_from_context_or_literal(v, stage_context, step_name_for_log) for k, v in value_template.items()} # Corrected scenario_context to stage_context
|
||
else:
|
||
return value_template # 其他类型直接返回
|
||
|
||
def _extract_outputs_to_context(self, response_content: Any, outputs_map: Dict[str, str], stage_context: Dict[str, Any], step_name_for_log: str):
|
||
"""
|
||
根据 outputs_map 从API响应中提取值并存入 stage_context。
|
||
Args:
|
||
response_content: API响应的内容 (通常是解析后的JSON字典)。
|
||
outputs_map: 定义如何提取的字典，例如 {"user_id": "data.id", "token": "header.X-Auth-Token"}。
|
||
支持 "body.", "header.", "status_code" 作为路径前缀。
|
||
stage_context: 要更新的阶段上下文。
|
||
step_name_for_log: 当前步骤名称，用于日志。
|
||
"""
|
||
if not outputs_map or response_content is None:
|
||
return
|
||
|
||
for context_var_name, extraction_path in outputs_map.items():
|
||
self.logger.debug(f"[阶段步骤 '{step_name_for_log}'] 尝试提取 '{extraction_path}' 到上下文变量 '{context_var_name}'")
|
||
value_to_extract = None
|
||
try:
|
||
current_data = response_content
|
||
path_parts = extraction_path.split('.')
|
||
|
||
source_type = path_parts[0].lower()
|
||
actual_path_parts = path_parts[1:]
|
||
|
||
if source_type == "body":
|
||
target_obj = current_data.get('json_content')
|
||
elif source_type == "header":
|
||
target_obj = current_data.get('headers')
|
||
elif source_type == "status_code":
|
||
if not actual_path_parts:
|
||
value_to_extract = current_data.get('status_code')
|
||
stage_context[context_var_name] = value_to_extract
|
||
self.logger.info(f"[阶段步骤 '{step_name_for_log}'] 提取到 '{context_var_name}': {value_to_extract}")
|
||
continue
|
||
else:
|
||
self.logger.warning(f"[阶段步骤 '{step_name_for_log}'] status_code 不支持进一步的路径提取: '{extraction_path}'")
|
||
continue
|
||
else:
|
||
self.logger.warning(f"[阶段步骤 '{step_name_for_log}'] 未知的提取源类型 '{source_type}' in path '{extraction_path}'")
|
||
continue
|
||
|
||
if target_obj is None and source_type != "status_code":
|
||
self.logger.warning(f"[阶段步骤 '{step_name_for_log}'] 提取源 '{source_type}' 为空或不存在。")
|
||
continue
|
||
|
||
temp_val = target_obj
|
||
for part in actual_path_parts:
|
||
if isinstance(temp_val, dict):
|
||
temp_val = temp_val.get(part)
|
||
elif isinstance(temp_val, list) and part.isdigit():
|
||
idx = int(part)
|
||
if 0 <= idx < len(temp_val):
|
||
temp_val = temp_val[idx]
|
||
else:
|
||
temp_val = None; break
|
||
else:
|
||
temp_val = None; break
|
||
if temp_val is None: break
|
||
|
||
value_to_extract = temp_val
|
||
|
||
if value_to_extract is not None:
|
||
stage_context[context_var_name] = value_to_extract
|
||
self.logger.info(f"[阶段步骤 '{step_name_for_log}'] 提取到上下文 '{context_var_name}': {str(value_to_extract)[:100]}...")
|
||
else:
|
||
self.logger.warning(f"[阶段步骤 '{step_name_for_log}'] 未能从路径 '{extraction_path}' 提取到值。")
|
||
except Exception as e:
|
||
self.logger.error(f"[阶段步骤 '{step_name_for_log}'] 从路径 '{extraction_path}' 提取值时出错: {e}", exc_info=True)
|
||
|
||
def execute_single_stage(self,
|
||
stage_instance: BaseAPIStage,
|
||
parsed_spec: ParsedAPISpec,
|
||
api_group_name: Optional[str]
|
||
) -> ExecutedStageResult:
|
||
stage_start_time = datetime.datetime.now()
|
||
stage_context: Dict[str, Any] = {}
|
||
executed_steps_results: List[ExecutedStageStepResult] = []
|
||
|
||
stage_result = ExecutedStageResult(
|
||
stage_id=stage_instance.id,
|
||
stage_name=stage_instance.name,
|
||
description=stage_instance.description,
|
||
api_group_metadata={"name": api_group_name} if api_group_name else None # <--- 修改参数名并包装为字典
|
||
# overall_status is set by default in __init__
|
||
)
|
||
|
||
try:
|
||
self.logger.debug(f"Calling before_stage for stage '{stage_instance.id}'. Context: {stage_context}")
|
||
# Ensure all parameters passed to before_stage are accepted by its definition
|
||
stage_instance.before_stage(stage_context=stage_context, global_api_spec=parsed_spec, api_group_name=api_group_name)
|
||
except Exception as e:
|
||
self.logger.error(f"Error in before_stage for stage '{stage_instance.id}': {e}", exc_info=True)
|
||
# stage_result.overall_status = ExecutedStageResult.Status.ERROR # This is already set if ERROR is present in Enum
|
||
# The following line should correctly set the status if an error occurs in before_stage
|
||
if hasattr(ExecutedStageResult.Status, 'ERROR'):
|
||
stage_result.overall_status = ExecutedStageResult.Status.ERROR
|
||
else: # Fallback if somehow ERROR is still not in the enum (should not happen now)
|
||
stage_result.overall_status = ExecutedStageResult.Status.FAILED
|
||
stage_result.message = f"before_stage hook failed: {e}"
|
||
stage_result.finalize_stage_result(final_context=stage_context) # <--- 更正方法名, 移除多余参数
|
||
return stage_result
|
||
|
||
# Assume PASSED, will be changed on any step failure/error not overridden by continue_on_failure logic
|
||
# The final status is determined more comprehensively at the end.
|
||
# stage_result.overall_status = ExecutedStageResult.Status.PASSED
|
||
|
||
for step_index, step_definition in enumerate(stage_instance.steps):
|
||
step_start_time = datetime.datetime.now()
|
||
step_name = step_definition.name or f"Step {step_index + 1}"
|
||
step_message = ""
|
||
step_validation_points: List[ValidationResult] = []
|
||
|
||
current_step_result = ExecutedStageStepResult(
|
||
step_name=step_name,
|
||
description=getattr(step_definition, 'description', None), # <--- 使用 getattr
|
||
lookup_key=step_definition.endpoint_spec_lookup_key,
|
||
status=ExecutedStageStepResult.Status.PENDING
|
||
# 其他参数 (如 resolved_endpoint, request_details, api_call_details, context_after_step)
|
||
# 会在步骤执行过程中或之后被填充到 current_step_result 对象上
|
||
)
|
||
|
||
try:
|
||
self.logger.debug(f"Calling before_step for stage '{stage_instance.id}', step '{step_name}'. Context: {stage_context}")
|
||
# Corrected: 'step' instead of 'step_definition' to match method signature
|
||
# Ensure all parameters passed are accepted by before_step definition
|
||
stage_instance.before_step(step=step_definition, stage_context=stage_context, global_api_spec=parsed_spec, api_group_name=api_group_name)
|
||
|
||
self.logger.info(f"Stage '{stage_instance.id}', Step '{step_name}': Looking up endpoint key='{step_definition.endpoint_spec_lookup_key}', group='{api_group_name}'")
|
||
api_op_spec: Optional[APIOperationSpec] = stage_instance.get_api_spec_for_operation(
|
||
lookup_key=step_definition.endpoint_spec_lookup_key, # Pass lookup_key by name
|
||
global_api_spec=parsed_spec, # Pass global_api_spec by name
|
||
api_group_name=api_group_name # Pass api_group_name by name
|
||
)
|
||
|
||
if not api_op_spec or not api_op_spec.spec:
|
||
current_step_result.status = ExecutedStageStepResult.Status.ERROR
|
||
current_step_result.message = f"Could not find API operation for key '{step_definition.endpoint_spec_lookup_key}' and group '{api_group_name}'."
|
||
self.logger.error(f"Stage '{stage_instance.id}', Step '{step_name}': {current_step_result.message}")
|
||
else:
|
||
actual_endpoint_spec_dict = api_op_spec.spec
|
||
current_step_result.resolved_endpoint = f"{api_op_spec.method.upper()} {api_op_spec.path}" if api_op_spec.method and api_op_spec.path else "Unknown Endpoint"
|
||
current_step_result.status = ExecutedStageStepResult.Status.PASSED # Assume pass, changed on failure
|
||
|
||
self.logger.debug(f"Stage '{stage_instance.id}', Step '{step_name}': Preparing request data for resolved endpoint: {current_step_result.resolved_endpoint}")
|
||
base_request_context: APIRequestContext = self._prepare_initial_request_data(actual_endpoint_spec_dict, None)
|
||
|
||
final_path_params = copy.deepcopy(base_request_context.path_params)
|
||
final_query_params = copy.deepcopy(base_request_context.query_params)
|
||
final_headers = copy.deepcopy(base_request_context.headers)
|
||
final_body = base_request_context.body
|
||
|
||
# Default Content-Type for JSON body if body is overridden and Content-Type not in headers
|
||
if "body" in step_definition.request_overrides:
|
||
temp_resolved_body_val = self._resolve_value_from_context_or_literal(
|
||
step_definition.request_overrides["body"], stage_context, step_name
|
||
)
|
||
# Check if Content-Type is already being set by header overrides
|
||
is_content_type_in_header_override = False
|
||
if "headers" in step_definition.request_overrides:
|
||
resolved_header_overrides = self._resolve_value_from_context_or_literal(
|
||
step_definition.request_overrides["headers"], stage_context, step_name
|
||
)
|
||
if isinstance(resolved_header_overrides, dict):
|
||
for h_key in resolved_header_overrides.keys():
|
||
if h_key.lower() == 'content-type':
|
||
is_content_type_in_header_override = True; break
|
||
|
||
# Check Content-Type in base_request_context.headers (after _prepare_initial_request_data)
|
||
is_content_type_in_final_headers = any(h_key.lower() == 'content-type' for h_key in final_headers.keys())
|
||
|
||
if isinstance(temp_resolved_body_val, (dict, list)) and not is_content_type_in_header_override and not is_content_type_in_final_headers:
|
||
final_headers['Content-Type'] = 'application/json'
|
||
self.logger.debug(f"Stage '{stage_instance.id}', Step '{step_name}': Defaulted Content-Type to application/json for overridden body.")
|
||
|
||
|
||
for key, value_template in step_definition.request_overrides.items():
|
||
resolved_value = self._resolve_value_from_context_or_literal(value_template, stage_context, step_name)
|
||
if key == "path_params":
|
||
if isinstance(resolved_value, dict): final_path_params.update(resolved_value)
|
||
else: self.logger.warning(f"Step '{step_name}': path_params override was not a dict (type: {type(resolved_value)}).")
|
||
elif key == "query_params":
|
||
if isinstance(resolved_value, dict): final_query_params.update(resolved_value)
|
||
else: self.logger.warning(f"Step '{step_name}': query_params override was not a dict (type: {type(resolved_value)}).")
|
||
elif key == "headers":
|
||
if isinstance(resolved_value, dict): final_headers.update(resolved_value) # Allows case-sensitive overrides if needed by server
|
||
else: self.logger.warning(f"Step '{step_name}': headers override was not a dict (type: {type(resolved_value)}).")
|
||
elif key == "body":
|
||
final_body = resolved_value
|
||
else:
|
||
self.logger.warning(f"Stage '{stage_instance.id}', Step '{step_name}': Unknown request override key '{key}'.")
|
||
|
||
# 构建完整的请求 URL
|
||
full_request_url = self._format_url_with_path_params(
|
||
path_template=api_op_spec.path,
|
||
path_params=final_path_params
|
||
)
|
||
self.logger.debug(f"Stage '{stage_instance.id}', Step '{step_name}': Constructed full_request_url: {full_request_url}")
|
||
|
||
api_request = APIRequest(
|
||
method=api_op_spec.method,
|
||
url=full_request_url, # <--- 使用构建好的完整 URL
|
||
params=final_query_params, # <--- query_params 对应 APIRequest 中的 params
|
||
headers=final_headers,
|
||
body=final_body # APIRequest 会通过 model_post_init 将 body 赋给 json_data
|
||
)
|
||
current_step_result.request_details = api_request.model_dump() # Use model_dump for Pydantic v2
|
||
|
||
self.logger.info(f"Stage '{stage_instance.id}', Step '{step_name}': Executing API call {api_request.method} {api_request.url}") # Log the full URL
|
||
api_response, api_call_detail = self.api_caller.call_api(api_request) # APICaller.call_api expects APIRequest
|
||
current_step_result.api_call_details = api_call_detail.model_dump() # Use model_dump for Pydantic v2
|
||
|
||
self.logger.debug(f"Stage '{stage_instance.id}', Step '{step_name}': Validating response. Status: {api_response.status_code}")
|
||
|
||
# Create APIResponseContext with the *actual* request sent
|
||
actual_request_context = APIRequestContext(
|
||
method=api_request.method,
|
||
url=str(api_request.url), # Pass the full URL string
|
||
path_params=final_path_params, # Keep for context, though URL is final
|
||
query_params=api_request.params, # query_params from APIRequest
|
||
headers=api_request.headers,
|
||
body=api_request.json_data, # body from APIRequest (after potential alias)
|
||
endpoint_spec=actual_endpoint_spec_dict # Ensure endpoint_spec is passed
|
||
# Removed full_url=str(api_call_detail.request_url) as it's redundant with 'url'
|
||
)
|
||
response_context = APIResponseContext(
|
||
request_context=actual_request_context,
|
||
status_code=api_response.status_code,
|
||
headers=api_response.headers,
|
||
json_content=api_response.json_content,
|
||
text_content=api_response.content.decode('utf-8', errors='replace') if api_response.content else None,
|
||
elapsed_time=api_response.elapsed_time,
|
||
original_response= getattr(api_response, 'raw_response', None)
|
||
# Removed endpoint_spec=actual_endpoint_spec_dict as it's part of actual_request_context
|
||
)
|
||
|
||
if step_definition.expected_status_codes and api_response.status_code not in step_definition.expected_status_codes: # Check against list
|
||
msg = f"Expected status code in {step_definition.expected_status_codes}, got {api_response.status_code}."
|
||
step_validation_points.append(ValidationResult(passed=False, message=msg, details={"expected": step_definition.expected_status_codes, "actual": api_response.status_code}))
|
||
current_step_result.status = ExecutedStageStepResult.Status.FAILED
|
||
step_message += msg + " "
|
||
self.logger.warning(f"Stage '{stage_instance.id}', Step '{step_name}': {msg}")
|
||
elif step_definition.expected_status_codes and api_response.status_code in step_definition.expected_status_codes:
|
||
step_validation_points.append(ValidationResult(passed=True, message=f"Status code matched ({api_response.status_code})."))
|
||
|
||
for i, assertion_func in enumerate(step_definition.response_assertions): # <--- Corrected: custom_assertions to response_assertions
|
||
assertion_name = getattr(assertion_func, '__name__', f"custom_assertion_{i+1}")
|
||
try:
|
||
self.logger.debug(f"Stage '{stage_instance.id}', Step '{step_name}': Running assertion '{assertion_name}'")
|
||
val_res = assertion_func(response_context, stage_context)
|
||
step_validation_points.append(val_res)
|
||
if not val_res.passed:
|
||
current_step_result.status = ExecutedStageStepResult.Status.FAILED
|
||
step_message += f"Assertion '{assertion_name}' failed: {val_res.message}. "
|
||
self.logger.warning(f"Stage '{stage_instance.id}', Step '{step_name}': Assertion '{assertion_name}' failed: {val_res.message}")
|
||
except Exception as assert_exc:
|
||
current_step_result.status = ExecutedStageStepResult.Status.ERROR
|
||
errMsg = f"Assertion '{assertion_name}' execution error: {assert_exc}"
|
||
step_message += errMsg + " "
|
||
step_validation_points.append(ValidationResult(passed=False, message=errMsg, details={"error": str(assert_exc)}))
|
||
self.logger.error(f"Stage '{stage_instance.id}', Step '{step_name}': {errMsg}", exc_info=True)
|
||
|
||
if current_step_result.status != ExecutedStageStepResult.Status.ERROR:
|
||
self.logger.debug(f"Stage '{stage_instance.id}', Step '{step_name}': Extracting outputs. Map: {step_definition.outputs_to_context}")
|
||
response_data_for_extraction = {
|
||
"json_content": api_response.json_content,
|
||
"headers": api_response.headers,
|
||
"status_code": api_response.status_code
|
||
}
|
||
self._extract_outputs_to_context(
|
||
response_data_for_extraction, step_definition.outputs_to_context,
|
||
stage_context, f"Stage '{stage_instance.id}', Step '{step_name}'"
|
||
)
|
||
current_step_result.context_after_step = copy.deepcopy(stage_context)
|
||
|
||
except Exception as step_exec_exc:
|
||
current_step_result.status = ExecutedStageStepResult.Status.ERROR
|
||
current_step_result.message = (current_step_result.message + f" | Unexpected error during step execution: {step_exec_exc}").strip()
|
||
self.logger.error(f"Stage '{stage_instance.id}', Step '{step_name}': {current_step_result.message}", exc_info=True)
|
||
|
||
finally:
|
||
current_step_result.duration_seconds = (datetime.datetime.now() - step_start_time).total_seconds()
|
||
current_step_result.message = (current_step_result.message + " " + step_message).strip()
|
||
current_step_result.validation_points = [vp.to_dict() for vp in step_validation_points]
|
||
|
||
try:
|
||
self.logger.debug(f"Calling after_step for stage '{stage_instance.id}', step '{step_name}'.")
|
||
# Corrected: Pass arguments by keyword to match signature and avoid positional errors
|
||
stage_instance.after_step(
|
||
step=step_definition,
|
||
step_result=current_step_result,
|
||
stage_context=stage_context,
|
||
global_api_spec=parsed_spec,
|
||
api_group_name=api_group_name
|
||
)
|
||
except Exception as e_as:
|
||
self.logger.error(f"Error in after_step for stage '{stage_instance.id}', step '{step_name}': {e_as}", exc_info=True)
|
||
if current_step_result.status == ExecutedStageStepResult.Status.PASSED:
|
||
current_step_result.status = ExecutedStageStepResult.Status.ERROR
|
||
current_step_result.message = (current_step_result.message + f" | after_step hook failed: {e_as}").strip()
|
||
elif current_step_result.message: current_step_result.message += f" | after_step hook failed: {e_as}"
|
||
else: current_step_result.message = f"after_step hook failed: {e_as}"
|
||
|
||
executed_steps_results.append(current_step_result)
|
||
|
||
if current_step_result.status != ExecutedStageStepResult.Status.PASSED:
|
||
if not stage_instance.continue_on_failure:
|
||
self.logger.warning(f"Stage '{stage_instance.id}', Step '{step_name}' status {current_step_result.status.value}, continue_on_failure=False. Aborting stage.")
|
||
# Update stage_result's overall status and message pre-emptively if aborting
|
||
if current_step_result.status == ExecutedStageStepResult.Status.ERROR:
|
||
stage_result.overall_status = ExecutedStageResult.Status.ERROR
|
||
stage_result.message = stage_result.message or f"Stage aborted due to error in step '{step_name}'."
|
||
elif current_step_result.status == ExecutedStageStepResult.Status.FAILED and stage_result.overall_status != ExecutedStageResult.Status.ERROR:
|
||
stage_result.overall_status = ExecutedStageResult.Status.FAILED
|
||
stage_result.message = stage_result.message or f"Stage aborted due to failure in step '{step_name}'."
|
||
break
|
||
|
||
# Determine final stage status
|
||
if stage_result.overall_status == ExecutedStageResult.Status.PENDING: # If not set by before_stage error or early abort
|
||
if not executed_steps_results and stage_instance.steps: # Steps defined but loop didn't run/finish (e.g. continue_on_failure issue)
|
||
stage_result.overall_status = ExecutedStageResult.Status.SKIPPED
|
||
stage_result.message = stage_result.message or "No steps were effectively executed."
|
||
elif not stage_instance.steps: # No steps defined for the stage
|
||
stage_result.overall_status = ExecutedStageResult.Status.PASSED # Considered PASSED if before_stage was OK
|
||
stage_result.message = stage_result.message or "Stage has no steps."
|
||
elif any(s.status == ExecutedStageStepResult.Status.ERROR for s in executed_steps_results):
|
||
stage_result.overall_status = ExecutedStageResult.Status.ERROR
|
||
stage_result.message = stage_result.message or "One or more steps encountered an error."
|
||
elif any(s.status == ExecutedStageStepResult.Status.FAILED for s in executed_steps_results):
|
||
stage_result.overall_status = ExecutedStageResult.Status.FAILED
|
||
stage_result.message = stage_result.message or "One or more steps failed."
|
||
elif all(s.status == ExecutedStageStepResult.Status.PASSED for s in executed_steps_results if executed_steps_results): # all steps passed
|
||
stage_result.overall_status = ExecutedStageResult.Status.PASSED
|
||
elif all(s.status == ExecutedStageStepResult.Status.SKIPPED for s in executed_steps_results if executed_steps_results): # all steps skipped
|
||
stage_result.overall_status = ExecutedStageResult.Status.SKIPPED
|
||
stage_result.message = stage_result.message or "All steps were skipped."
|
||
else: # Mix of PASSED, SKIPPED, etc., but no FAILED or ERROR that wasn't handled by continue_on_failure
|
||
# This case implies successful completion if no explicit FAILED/ERROR states propagated.
|
||
# If there are executed steps, and none failed or errored, it's a pass.
|
||
# If all executed steps passed or were skipped, and at least one passed: PASSED
|
||
# If all executed steps were skipped: SKIPPED
|
||
has_passed_step = any(s.status == ExecutedStageStepResult.Status.PASSED for s in executed_steps_results)
|
||
if has_passed_step:
|
||
stage_result.overall_status = ExecutedStageResult.Status.PASSED
|
||
else: # No errors, no failures, no passes, implies all skipped or pending (which shouldn't happen for completed steps)
|
||
stage_result.overall_status = ExecutedStageResult.Status.SKIPPED
|
||
stage_result.message = stage_result.message or "Steps completed without explicit pass, fail, or error."
|
||
|
||
|
||
try:
|
||
self.logger.debug(f"Calling after_stage for stage '{stage_instance.id}'.")
|
||
stage_instance.after_stage(stage_result=stage_result, stage_context=stage_context, global_api_spec=parsed_spec, api_group_name=api_group_name)
|
||
except Exception as e_asg:
|
||
self.logger.error(f"Error in after_stage for stage '{stage_instance.id}': {e_asg}", exc_info=True)
|
||
if stage_result.overall_status not in [ExecutedStageResult.Status.ERROR]: # Don't override a more severe status
|
||
original_status_msg = f"(Original status: {stage_result.overall_status.value})" if stage_result.overall_status != ExecutedStageResult.Status.PASSED else ""
|
||
stage_result.overall_status = ExecutedStageResult.Status.ERROR
|
||
current_msg = stage_result.message if stage_result.message else ""
|
||
stage_result.message = f"{current_msg} after_stage hook failed: {e_asg} {original_status_msg}".strip()
|
||
elif stage_result.message: stage_result.message += f" | after_stage hook failed: {e_asg}"
|
||
else: stage_result.message = f"after_stage hook failed: {e_asg}"
|
||
|
||
stage_result.finalize_stage_result(final_context=stage_context)
|
||
self.logger.info(f"Stage '{stage_instance.id}' execution finished. API Group: '{api_group_name}', Final Status: {stage_result.overall_status.value}, Duration: {stage_result.duration:.2f}s") # Corrected duration_seconds to duration
|
||
return stage_result
|
||
|
||
def run_stages_from_spec(self,
|
||
parsed_spec: ParsedAPISpec,
|
||
summary: TestSummary):
|
||
self.logger.info("Starting API Test Stage execution...")
|
||
if not self.stage_registry or not self.stage_registry.get_all_stage_classes():
|
||
self.logger.info("No API Test Stages loaded. Skipping stage execution.")
|
||
return
|
||
|
||
stage_classes = self.stage_registry.get_all_stage_classes()
|
||
summary.set_total_stages_defined(len(stage_classes))
|
||
|
||
api_groups: List[Optional[str]] = []
|
||
if isinstance(parsed_spec, ParsedYAPISpec):
|
||
# Use parsed_spec.categories which are List[Dict[str, str]] from YAPIInput
|
||
if parsed_spec.categories:
|
||
api_groups.extend([cat.get('name') for cat in parsed_spec.categories if cat.get('name')])
|
||
# If categories list exists but all are unnamed or empty, or if no categories attribute
|
||
if not api_groups and (not hasattr(parsed_spec, 'categories') or parsed_spec.categories is not None):
|
||
self.logger.info("YAPI spec: No named categories found or categories attribute missing/empty. Applying stages to the whole spec (api_group_name=None).")
|
||
api_groups.append(None)
|
||
elif isinstance(parsed_spec, ParsedSwaggerSpec):
|
||
# Use parsed_spec.tags which are List[Dict[str, str]]
|
||
if parsed_spec.tags:
|
||
api_groups.extend([tag.get('name') for tag in parsed_spec.tags if tag.get('name')])
|
||
if not api_groups and (not hasattr(parsed_spec, 'tags') or parsed_spec.tags is not None):
|
||
self.logger.info("Swagger spec: No named tags found or tags attribute missing/empty. Applying stages to the whole spec (api_group_name=None).")
|
||
api_groups.append(None)
|
||
|
||
if not api_groups: # Default for other spec types or if above logic resulted in empty list
|
||
self.logger.info("No specific API groups (categories/tags) identified. Applying stages to the whole spec (api_group_name=None).")
|
||
api_groups.append(None)
|
||
|
||
self.logger.info(f"Evaluating test stages against {len(api_groups)} API group(s): {api_groups}")
|
||
|
||
total_stages_considered_for_execution = 0
|
||
|
||
for stage_class in stage_classes:
|
||
# For template instance, provide default/empty metadata and api lists
|
||
# as it's mainly used for accessing static-like properties before group-specific execution.
|
||
default_group_meta = {"name": "Template Group", "description": "Used for stage template loading"}
|
||
stage_instance_template = stage_class(
|
||
api_group_metadata=default_group_meta,
|
||
apis_in_group=[],
|
||
llm_service=self.llm_service,
|
||
global_api_spec=parsed_spec # Template might still need global spec for some pre-checks
|
||
)
|
||
self.logger.info(f"Processing Test Stage definition: ID='{stage_instance_template.id}', Name='{stage_instance_template.name}'")
|
||
|
||
was_applicable_and_executed_at_least_once = False
|
||
|
||
for api_group_name in api_groups:
|
||
# Prepare api_group_metadata and apis_in_group for the current group
|
||
current_group_metadata: Dict[str, Any] = {}
|
||
current_apis_in_group_dicts: List[Dict[str, Any]] = []
|
||
|
||
if api_group_name:
|
||
if isinstance(parsed_spec, ParsedYAPISpec) and parsed_spec.spec and 'categories' in parsed_spec.spec:
|
||
category_details_list = parsed_spec.spec.get('categories', [])
|
||
category_detail = next((cat for cat in category_details_list if cat.get('name') == api_group_name), None)
|
||
if category_detail:
|
||
current_group_metadata = {"name": api_group_name,
|
||
"description": category_detail.get("desc"),
|
||
"id": category_detail.get("_id")}
|
||
category_id_to_match = category_detail.get("_id")
|
||
# Filter endpoints for this category by id
|
||
current_apis_in_group_dicts = [
|
||
ep.to_dict() for ep in parsed_spec.endpoints
|
||
if hasattr(ep, 'category_id') and ep.category_id == category_id_to_match
|
||
]
|
||
else:
|
||
self.logger.warning(f"Could not find details for YAPI category: {api_group_name} in parsed_spec.spec.categories. API list for this group might be empty.")
|
||
current_group_metadata = {"name": api_group_name, "description": "Details not found in spec top-level categories"}
|
||
current_apis_in_group_dicts = [] # Or could attempt to filter by name if IDs are unreliable
|
||
|
||
elif isinstance(parsed_spec, ParsedSwaggerSpec):
|
||
# For Swagger, tags on operations are primary; global tags are for definition.
|
||
current_group_metadata = {"name": api_group_name}
|
||
if parsed_spec.spec and 'tags' in parsed_spec.spec:
|
||
tag_detail = next((tag for tag in parsed_spec.spec.get('tags', []) if tag.get('name') == api_group_name), None)
|
||
if tag_detail:
|
||
current_group_metadata["description"] = tag_detail.get("description")
|
||
else: # Tag name exists (from api_groups list) but not in the top-level tags definitions
|
||
current_group_metadata["description"] = "Tag defined on operation, not in global tags list."
|
||
|
||
# Filter endpoints for this tag
|
||
current_apis_in_group_dicts = [
|
||
ep.to_dict() for ep in parsed_spec.endpoints
|
||
if hasattr(ep, 'tags') and isinstance(ep.tags, list) and api_group_name in ep.tags
|
||
]
|
||
else:
|
||
self.logger.warning(f"API group '{api_group_name}' provided, but cannot determine group details or filter APIs for spec type {type(parsed_spec)}.")
|
||
current_group_metadata = {"name": api_group_name, "description": "Unknown group type or spec structure"}
|
||
current_apis_in_group_dicts = [ep.to_dict() for ep in parsed_spec.endpoints]
|
||
|
||
else: # api_group_name is None (global scope)
|
||
current_group_metadata = {"name": "Global (All APIs)", "description": "Applies to all APIs in the spec"}
|
||
current_apis_in_group_dicts = [ep.to_dict() for ep in parsed_spec.endpoints]
|
||
|
||
# Instantiate the stage with the prepared context for the current API group
|
||
stage_instance = stage_class(
|
||
api_group_metadata=current_group_metadata,
|
||
apis_in_group=current_apis_in_group_dicts,
|
||
llm_service=self.llm_service,
|
||
global_api_spec=parsed_spec
|
||
)
|
||
total_stages_considered_for_execution += 1
|
||
|
||
try:
|
||
self.logger.debug(f"Checking applicability of stage '{stage_instance.id}' for API group '{api_group_name}'...")
|
||
applicable = stage_instance.is_applicable_to_api_group(
|
||
api_group_name=api_group_name,
|
||
global_api_spec=parsed_spec
|
||
)
|
||
except Exception as e:
|
||
self.logger.error(f"Error checking applicability of stage '{stage_instance.id}' for group '{api_group_name}': {e}", exc_info=True)
|
||
error_result = ExecutedStageResult(
|
||
stage_id=stage_instance.id, stage_name=stage_instance.name, api_group=api_group_name,
|
||
overall_status=ExecutedStageResult.Status.ERROR, message=f"Error during applicability check: {e}"
|
||
)
|
||
error_result.finalize_result(datetime.datetime.now(), [], {})
|
||
summary.add_stage_result(error_result)
|
||
was_applicable_and_executed_at_least_once = True # Considered an attempt
|
||
continue
|
||
|
||
if applicable:
|
||
self.logger.info(f"Test Stage '{stage_instance.id}' is APPLICABLE to API group '{api_group_name}'. Executing...")
|
||
stage_execution_result = self.execute_single_stage(stage_instance, parsed_spec, api_group_name)
|
||
summary.add_stage_result(stage_execution_result)
|
||
was_applicable_and_executed_at_least_once = True
|
||
else:
|
||
self.logger.info(f"Test Stage '{stage_instance.id}' is NOT APPLICABLE to API group '{api_group_name}'. Skipping for this group.")
|
||
|
||
if not was_applicable_and_executed_at_least_once and stage_instance_template.fail_if_not_applicable_to_any_group:
|
||
self.logger.warning(f"Test Stage '{stage_instance_template.id}' was not applicable to any API group and 'fail_if_not_applicable_to_any_group' is True.")
|
||
failure_result = ExecutedStageResult(
|
||
stage_id=stage_instance_template.id, stage_name=stage_instance_template.name, api_group=None,
|
||
overall_status=ExecutedStageResult.Status.FAILED,
|
||
message=f"Stage marked as 'must apply' but was not applicable to any of the evaluated groups: {api_groups}."
|
||
)
|
||
failure_result.finalize_result(datetime.datetime.now(), [], {})
|
||
summary.add_stage_result(failure_result)
|
||
|
||
self.logger.info(f"API Test Stage execution processed. Considered {total_stages_considered_for_execution} (stage_definition x api_group) combinations.")
|
||
|
||
def _execute_tests_from_parsed_spec(self,
|
||
parsed_spec: ParsedAPISpec,
|
||
summary: TestSummary,
|
||
categories: Optional[List[str]] = None,
|
||
tags: Optional[List[str]] = None,
|
||
custom_test_cases_dir: Optional[str] = None
|
||
) -> TestSummary:
|
||
"""基于已解析的API规范对象执行测试用例。"""
|
||
# Restore the original start of the method body, the rest of the method should be intact from before.
|
||
if custom_test_cases_dir and (not self.test_case_registry or not hasattr(self.test_case_registry, 'test_cases_dir') or self.test_case_registry.test_cases_dir != custom_test_cases_dir):
|
||
self.logger.info(f"Re-initializing TestCaseRegistry from _execute_tests_from_parsed_spec with new directory: {custom_test_cases_dir}")
|
||
try:
|
||
# Assuming TestCaseRegistry can be re-initialized or its directory updated.
|
||
# If TestCaseRegistry is loaded in __init__, this might need adjustment
|
||
# For now, let's assume direct re-init is possible if dir changes.
|
||
self.test_case_registry = TestCaseRegistry()
|
||
self.test_case_registry.discover_and_load_test_cases(custom_test_cases_dir)
|
||
self.logger.info(f"TestCaseRegistry (re)initialized, found {len(self.test_case_registry.get_all_test_case_classes())} test case classes.")
|
||
except Exception as e:
|
||
self.logger.error(f"Failed to re-initialize TestCaseRegistry from _execute_tests_from_parsed_spec: {e}", exc_info=True)
|
||
# summary.finalize_summary() # Finalize might be premature here
|
||
return summary # Early exit if registry fails
|
||
|
||
endpoints_to_test: List[Union[YAPIEndpoint, SwaggerEndpoint]] = []
|
||
if isinstance(parsed_spec, ParsedYAPISpec):
|
||
endpoints_to_test = parsed_spec.endpoints
|
||
if categories:
|
||
# Ensure YAPIEndpoint has 'category_name' if this filter is used.
|
||
endpoints_to_test = [ep for ep in endpoints_to_test if hasattr(ep, 'category_name') and ep.category_name in categories]
|
||
elif isinstance(parsed_spec, ParsedSwaggerSpec):
|
||
endpoints_to_test = parsed_spec.endpoints
|
||
if tags:
|
||
# Ensure SwaggerEndpoint has 'tags' attribute for this filter.
|
||
endpoints_to_test = [ep for ep in endpoints_to_test if hasattr(ep, 'tags') and isinstance(ep.tags, list) and any(tag in ep.tags for tag in tags)]
|
||
else:
|
||
self.logger.warning(f"Unknown parsed_spec type: {type(parsed_spec)}. Cannot filter endpoints.")
|
||
# summary.finalize_summary() # Finalize might be premature
|
||
return summary
|
||
|
||
current_total_defined = summary.total_endpoints_defined
|
||
summary.set_total_endpoints_defined(current_total_defined + len(endpoints_to_test))
|
||
|
||
total_applicable_tcs_for_this_run = 0
|
||
if self.test_case_registry:
|
||
for endpoint_spec_obj in endpoints_to_test:
|
||
total_applicable_tcs_for_this_run += len(
|
||
self.test_case_registry.get_applicable_test_cases(
|
||
endpoint_spec_obj.method.upper(), endpoint_spec_obj.path
|
||
)
|
||
)
|
||
current_total_applicable = summary.total_test_cases_applicable
|
||
summary.set_total_test_cases_applicable(current_total_applicable + total_applicable_tcs_for_this_run)
|
||
|
||
for endpoint in endpoints_to_test:
|
||
# global_api_spec 应该是包含完整定义的 ParsedYAPISpec/ParsedSwaggerSpec 对象
|
||
# 而不是其内部的 .spec 字典，因为 _execute_single_test_case 需要这个对象
|
||
result = self.run_test_for_endpoint(endpoint, global_api_spec=parsed_spec)
|
||
summary.add_endpoint_result(result)
|
||
|
||
return summary
|
||
|